Posts in category: Google AI

Posted by Yanqi Zhou, Research Scientist, Google Research Brain Team

The capacity of a neural network to absorb information is limited by the number of its parameters, and as a consequence, finding more effective ways to increase model parameters has become a trend in deep learning research. Mixture-of-experts (MoE), a type of conditional computation where parts of the network are activated on a per-example basis, has been proposed as a way of dramatically increasing model capacity without a proportional increase in computation. In sparsely-activated variants of MoE models (e.g., Switch Transformer, GLaM, V-MoE), a subset of experts is selected on a per-token or per-example basis, thus creating sparsity in the network. Such models have demonstrated better scaling in multiple domains and better retention capability in a continual learning setting (e.g., Expert Gate). However, a poor expert routing strategy can cause certain experts to be under-trained, leading to an expert being under or over-specialized.

In “Mixture-of-Experts with Expert Choice Routing”, presented at NeurIPS 2022, we introduce a novel MoE routing algorithm called Expert Choice (EC). We discuss how this novel approach can achieve optimal load balancing in an MoE system while allowing heterogeneity in token-to-expert mapping. Compared to token-based routing and other routing methods in traditional MoE networks, EC demonstrates very strong training efficiency and downstream task scores. Our method resonates with one of the vision for Pathways, which is to enable heterogeneous mixture-of-experts via Pathways MPMD (multi program, multi data) support.

Overview of MoE Routing

MoE operates by adopting a number of experts, each as a sub-network, and activating only one or a few experts for each input token. A gating network must be chosen and optimized in order to route each token to the most suited expert(s). Depending on how tokens are mapped to experts, MoE can be sparse or dense. Sparse MoE only selects a subset of experts when routing each token, reducing computational cost as compared to a dense MoE. For example, recent work has implemented sparse routing via k-means clustering, linear assignment to maximize token-expert affinities, or hashing. Google also recently announced GLaM and V-MoE, both of which advance the state of the art in natural language processing and computer vision via sparsely gated MoE with top-k token routing, demonstrating better performance scaling with sparsely activated MoE layers. Many of these prior works used a token choice routing strategy in which the routing algorithm picks the best one or two experts for each token.

Token Choice Routing. The routing algorithm picks the top-1 or top-2 experts with highest affinity scores for each token. The affinity scores can be trained together with model parameters.

The independent token choice approach often leads to an imbalanced load of experts and under-utilization. In order to mitigate this, previous sparsely gated networks introduced additional auxiliary losses as regularization to prevent too many tokens being routed to a single expert, but the effectiveness was limited. As a result, token choice routings need to overprovision expert capacity by a significant margin (2x–8x of the calculated capacity) to avoid dropping tokens when there is a buffer overflow.

In addition to load imbalance, most prior works allocate a fixed number of experts to each token using a top-k function, regardless of the relative importance of different tokens. We argue that different tokens should be received by a variable number of experts, conditioned on token importance or difficulty.

Expert Choice Routing

To address the above issues, we propose a heterogeneous MoE that employs the expert choice routing method illustrated below. Instead of having tokens select the top-k experts, the experts with predetermined buffer capacity are assigned to the top-k tokens. This method guarantees even load balancing, allows a variable number of experts for each token, and achieves substantial gains in training efficiency and downstream performance. EC routing speeds up training convergence by over 2x in an 8B/64E (8 billion activated parameters, 64 experts) model, compared to the top-1 and top-2 gating counterparts in Switch Transformer, GShard, and GLaM.

Expert Choice Routing. Experts with predetermined buffer capacity are assigned top-k tokens, thus guaranteeing even load balancing. Each token can be received by a variable number of experts.

In EC routing, we set expert capacity k as the average tokens per expert in a batch of input sequences multiplied by a capacity factor, which determines the average number of experts that can be received by each token. To learn the token-to-expert affinity, our method produces a token-to-expert score matrix that is used to make routing decisions. The score matrix indicates the likelihood of a given token in a batch of input sequences being routed to a given expert.

Similar to Switch Transformer and GShard, we apply an MoE and gating function in the dense feedforward (FFN) layer, as it is the most computationally expensive part of a Transformer-based network. After producing the token-to-expert score matrix, a top-k function is applied along the token dimension for each expert to pick the most relevant tokens. A permutation function is then applied based on the generated indexes of the token, to create a hidden value with an additional expert dimension. The data is split across multiple experts such that all experts can execute the same computational kernel concurrently on a subset of tokens. Because a fixed expert capacity can be determined, we no longer overprovision expert capacity due to load imbalancing, thus significantly reducing training and inference step time by around 20% compared to GLaM.

Evaluation

To illustrate the effectiveness of Expert Choice routing, we first look at training efficiency and convergence. We use EC with a capacity factor of 2 (EC-CF2) to match the activated parameter size and computational cost on a per-token basis to GShard top-2 gating and run both for a fixed number of steps. EC-CF2 reaches the same perplexity as GShard top-2 in less than half the steps and, in addition, we find that each GShard top-2 step is 20% slower than our method.

We also scale the number of experts while fixing the expert size to 100M parameters for both EC and GShard top-2 methods. We find that both work well in terms of perplexity on the evaluation dataset during pre-training — having more experts consistently improves training perplexity.

Evaluation results on training convergence: EC routing yields 2x faster convergence at 8B/64E scale compared to top-2 gating used in GShard and GLaM (top). EC training perplexity scales better with the scaling of number of experts (bottom).

To validate whether improved perplexity directly translates to better performance in downstream tasks, we perform fine-tuning on 11 selected tasks from GLUE and SuperGLUE. We compare three MoE methods including Switch Transformer top-1 gating (ST Top-1), GShard top-2 gating (GS Top-2) and a version of our method (EC-CF2) that matches the activated parameters and computational cost of GS Top-2. The EC-CF2 method consistently outperforms the related methods and yields an average accuracy increase of more than 2% in a large 8B/64E setting. Comparing our 8B/64E model against its dense counterpart, our method achieves better fine-tuning results, increasing the average score by 3.4 points.

Our empirical results indicate that capping the number of experts for each token hurts the fine-tuning score by 1 point on average. This study confirms that allowing a variable number of experts per token is indeed helpful. On the other hand, we compute statistics on token-to-expert routing, particularly on the ratio of tokens that have been routed to a certain number of experts. We find that a majority of tokens have been routed to one or two experts while 23% have been routed to three or four experts and only about 3% tokens have been routed to more than four experts, thus verifying our hypothesis that expert choice routing learns to allocate a variable number of experts to tokens.

Final Thoughts

We propose a new routing method for sparsely activated mixture-of-experts models. This method addresses load imbalance and under-utilization of experts in conventional MoE methods, and enables the selection of different numbers of experts for each token. Our model demonstrates more than 2x training efficiency improvement when compared to the state-of-the-art GShard and Switch Transformer models, and achieves strong gains when fine-tuning on 11 datasets in the GLUE and SuperGLUE benchmark.

Our approach for expert choice routing enables heterogeneous MoE with straightforward algorithmic innovations. We hope that this may lead to more advances in this space at both the application and system levels.

Acknowledgements

Many collaborators across google research supported this work. We particularly thank Nan Du, Andrew Dai, Yanping Huang, and Zhifeng Chen for the initial ground work on MoE infrastructure and Tarzan datasets. We greatly appreciate Hanxiao Liu and Quoc Le for contributing the initial ideas and discussions. Tao Lei, Vincent Zhao, Da Huang, Chang Lan, Daiyi Peng, and Yifeng Lu contributed significantly on implementations and evaluations. Claire Cui, James Laudon, Martin Abadi, and Jeff Dean provided invaluable feedback and resource support.

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Posted by Jason Wei and Yi Tay, Research Scientists, Google Research, Brain Team

The field of natural language processing (NLP) has been revolutionized by language models trained on large amounts of text data. Scaling up the size of language models often leads to improved performance and sample efficiency on a range of downstream NLP tasks. In many cases, the performance of a large language model can be predicted by extrapolating the performance trend of smaller models. For instance, the effect of scale on language model perplexity has been empirically shown to span more than seven orders of magnitude.

On the other hand, performance for certain other tasks does not improve in a predictable fashion. For example, the GPT-3 paper showed that the ability of language models to perform multi-digit addition has a flat scaling curve (approximately random performance) for models from 100M to 13B parameters, at which point the performance jumped substantially. Given the growing use of language models in NLP research and applications, it is important to better understand abilities such as these that can arise unexpectedly.

In “Emergent Abilities of Large Language Models,” recently published in the Transactions on Machine Learning Research (TMLR), we discuss the phenomena of emergent abilities, which we define as abilities that are not present in small models but are present in larger models. More specifically, we study emergence by analyzing the performance of language models as a function of language model scale, as measured by total floating point operations (FLOPs), or how much compute was used to train the language model. However, we also explore emergence as a function of other variables, such as dataset size or number of model parameters (see the paper for full details). Overall, we present dozens of examples of emergent abilities that result from scaling up language models. The existence of such emergent abilities raises the question of whether additional scaling could potentially further expand the range of capabilities of language models.

Emergent Prompted Tasks

First we discuss emergent abilities that may arise in prompted tasks. In such tasks, a pre-trained language model is given a prompt for a task framed as next word prediction, and it performs the task by completing the response. Without any further fine-tuning, language models can often perform tasks that were not seen during training.

Example of few-shot prompting on movie review sentiment classification. The model is given one example of a task (classifying a movie review as positive or negative) and then performs the task on an unseen example.

We call a prompted task emergent when it unpredictably surges from random performance to above-random at a specific scale threshold. Below we show three examples of prompted tasks with emergent performance: multi-step arithmetic, taking college-level exams, and identifying the intended meaning of a word. In each case, language models perform poorly with very little dependence on model size up to a threshold at which point their performance suddenly begins to excel.

The ability to perform multi-step arithmetic (left), succeed on college-level exams (middle), and identify the intended meaning of a word in context (right) all emerge only for models of sufficiently large scale. The models shown include LaMDA, GPT-3, Gopher, Chinchilla, and PaLM.

Performance on these tasks only becomes non-random for models of sufficient scale — for instance, above 10²² training FLOPs for the arithmetic and multi-task NLU tasks, and above 10²⁴ training FLOPs for the word in context tasks. Note that although the scale at which emergence occurs can be different for different tasks and models, no model showed smooth improvement in behavior on any of these tasks. Dozens of other emergent prompted tasks are listed in our paper.

Emergent Prompting Strategies

The second class of emergent abilities encompasses prompting strategies that augment the capabilities of language models. Prompting strategies are broad paradigms for prompting that can be applied to a range of different tasks. They are considered emergent when they fail for small models and can only be used by a sufficiently-large model.

One example of an emergent prompting strategy is called “chain-of-thought prompting”, for which the model is prompted to generate a series of intermediate steps before giving the final answer. Chain-of-thought prompting enables language models to perform tasks requiring complex reasoning, such as a multi-step math word problem. Notably, models acquire the ability to do chain-of-thought reasoning without being explicitly trained to do so. An example of chain-of-thought prompting is shown in the figure below.

Chain of thought prompting enables sufficiently large models to solve multi-step reasoning problems.

The empirical results of chain-of-thought prompting are shown below. For smaller models, applying chain-of-thought prompting does not outperform standard prompting, for example, when applied to GSM8K, a challenging benchmark of math word problems. However, for large models (10²⁴ FLOPs), chain-of-thought prompting substantially improves performance in our tests, reaching a 57% solve rate on GSM8K.

Chain-of-thought prompting is an emergent ability — it fails to improve performance for small language models, but substantially improves performance for large models. Here we illustrate the difference between standard and chain-of-thought prompting at different scales for two language models, LaMDA and PaLM.

Implications of Emergent Abilities

The existence of emergent abilities has a range of implications. For example, because emergent few-shot prompted abilities and strategies are not explicitly encoded in pre-training, researchers may not know the full scope of few-shot prompted abilities of current language models. Moreover, the emergence of new abilities as a function of model scale raises the question of whether further scaling will potentially endow even larger models with new emergent abilities.

Identifying emergent abilities in large language models is a first step in understanding such phenomena and their potential impact on future model capabilities. Why does scaling unlock emergent abilities? Because computational resources are expensive, can emergent abilities be unlocked via other methods without increased scaling (e.g., better model architectures or training techniques)? Will new real-world applications of language models become unlocked when certain abilities emerge? Analyzing and understanding the behaviors of language models, including emergent behaviors that arise from scaling, is an important research question as the field of NLP continues to grow.

Acknowledgements

It was an honor and privilege to work with Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus.

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Posted by Peter H. Li, Research Scientist, and Sven Dorkenwald, Student Researcher, Connectomics at Google

Mapping the wiring and firing activity of the human brain is fundamental to deciphering how we think — how we sense the world, learn, decide, remember, and create — as well as what issues can arise in brain disease or dysfunction. Recent efforts have delivered publicly available brain maps (high-resolution 3D mapping of brain cells and their connectivities) at unprecedented quality and scale, such as H01, a 1.4 petabyte nanometer-scale digital reconstruction of a sample of human brain tissue from Harvard / Google, and the cubic millimeter mouse cortex dataset from our colleagues at the MICrONS consortium.

To interpret brain maps at this scale requires multiple layers of analysis, including the identification of synaptic connections, cellular subcompartments, and cell types. Machine learning and computer vision technology have played a central role in enabling these analyses, but deploying such systems is still a laborious process, requiring hours of manual ground truth labeling by expert annotators and significant computational resources. Moreover, some important tasks, such as identifying the cell type from only a small fragment of axon or dendrite, can be challenging even for human experts, and have not yet been effectively automated.

Today, in “Multi-Layered Maps of Neuropil with Segmentation-Guided Contrastive Learning”, we are announcing Segmentation-Guided Contrastive Learning of Representations (SegCLR), a method for training rich, generic representations of cellular morphology (the cell’s shape) and ultrastructure (the cell’s internal structure) without laborious manual effort. SegCLR produces compact vector representations (i.e., embeddings) that are applicable across diverse downstream tasks (e.g., local classification of cellular subcompartments, unsupervised clustering), and are even able to identify cell types from only small fragments of a cell. We trained SegCLR on both the H01 human cortex dataset and the MICrONS mouse cortex dataset, and we are releasing the resulting embedding vectors, about 8 billion in total, for researchers to explore.

Representing Cellular Morphology and Ultrastructure

SegCLR builds on recent advances in self-supervised contrastive learning. We use a standard deep network architecture to encode inputs comprising local 3D blocks of electron microscopy data (about 4 micrometers on a side) into 64-dimensional embedding vectors. The network is trained via a contrastive loss to map semantically related inputs to similar coordinates in the embedding space. This is close to the popular SimCLR setup, except that we also require an instance segmentation of the volume (tracing out individual cells and cell fragments), which we use in two important ways.

First, the input 3D electron microscopy data are explicitly masked by the segmentation, forcing the network to focus only on the central cell within each block. Second, we leverage the segmentation to automatically define which inputs are semantically related: positive pairs for the contrastive loss are drawn from nearby locations on the same segmented cell and trained to have similar representations, while inputs drawn from different cells are trained to have dissimilar representations. Importantly, publicly available automated segmentations of the human and mouse datasets were sufficiently accurate to train SegCLR without requiring laborious review and correction by human experts.

SegCLR is trained to represent rich cellular features without manual labeling. Top: The SegCLR architecture maps local masked 3D views of electron microscopy data to embedding vectors. Only the microscopy volume and a draft automated instance segmentation are required. Bottom: The segmentation is also used to define positive versus negative example pairs, whose representations are pushed closer together (positives, blue arrows) or further apart (negatives, red arrows) during training.

Reducing Annotation Training Requirements by Three Orders of Magnitude

SegCLR embeddings can be used in diverse downstream settings, whether supervised (e.g., training classifiers) or unsupervised (e.g., clustering or content-based image retrieval). In the supervised setting, embeddings simplify the training of classifiers, and can greatly reduce ground truth labeling requirements. For example, we found that for identifying cellular subcompartments (axon, dendrite, soma, etc.) a simple linear classifier trained on top of SegCLR embeddings outperformed a fully supervised deep network trained on the same task, while using only about one thousand labeled examples instead of millions.

We assessed the classification performance for axon, dendrite, soma, and astrocyte subcompartments in the human cortex dataset via mean F1-Score, while varying the number of training examples used. Linear classifiers trained on top of SegCLR embeddings matched or exceeded the performance of a fully supervised deep classifier (horizontal line), while using a fraction of the training data.

Distinguishing Cell Types, Even from Small Fragments

Distinguishing different cell types is an important step towards understanding how brain circuits develop and function in health and disease. Human experts can learn to identify some cortical cell types based on morphological features, but manual cell typing is laborious and ambiguous cases are common. Cell typing also becomes more difficult when only small fragments of cells are available, which is common for many cells in current connectomic reconstructions.

Human experts manually labeled cell types for a small number of proofread cells in each dataset. In the mouse cortex dataset, experts labeled six neuron types (top) and four glia types (not shown). In the human cortex dataset, experts labeled two neuron types (not shown) and four glia types (bottom). (Rows not to scale with each other.)

We found that SegCLR accurately infers human and mouse cell types, even for small fragments. Prior to classification, we collected and averaged embeddings within each cell over a set aggregation distance, defined as the radius from a central point. We found that human cortical cell types can be identified with high accuracy for aggregation radii as small as 10 micrometers, even for types that experts find difficult to distinguish, such as microglia (MGC) versus oligodendrocyte precursor cells (OPC).

SegCLR can classify cell types, even from small fragments. Left: Classification performance over six human cortex cell types for shallow ResNet models trained on SegCLR embeddings for different sized cell fragments. Aggregation radius zero corresponds to very small fragments with only a single embedding. Cell type performance reaches high accuracy (0.938 mean F1-Score) for fragments with aggregation radii of only 10 micrometers (boxed point). Right: Class-wise confusion matrix at 10 micrometers aggregation radius. Darker shading along the diagonal indicates that predicted cell types agree with expert labels in most cases. AC: astrocyte; MGC: microglia cell; OGC: oligodendrocyte cell; OPC: oligodendrocyte precursor cell; E: excitatory neuron; I: inhibitory neuron.

In the mouse cortex, ten cell types could be distinguished with high accuracy at aggregation radii of 25 micrometers.

Left: Classification performance over the ten mouse cortex cell types reaches 0.832 mean F1-Score for fragments with aggregation radius 25 micrometers (boxed point). Right: The class-wise confusion matrix at 25 micrometers aggregation radius. Boxes indicate broad groups (glia, excitatory neurons, and inhibitory interneurons). P: pyramidal cell; THLC: thalamocortical axon; BC: basket cell; BPC: bipolar cell; MC: Martinotti cell; NGC: neurogliaform cell.

In additional cell type applications, we used unsupervised clustering of SegCLR embeddings to reveal further neuronal subtypes, and demonstrated how uncertainty estimation can be used to restrict classification to high confidence subsets of the dataset, e.g., when only a few cell types have expert labels.

Revealing Patterns of Brain Connectivity

Finally, we showed how SegCLR can be used for automated analysis of brain connectivity by cell typing the synaptic partners of reconstructed cells throughout the mouse cortex dataset. Knowing the connectivity patterns between specific cell types is fundamental to interpreting large-scale connectomic reconstructions of brain wiring, but this typically requires manual tracing to identify partner cell types. Using SegCLR, we replicated brain connectivity findings that previously relied on intensive manual tracing, while extending their scale in terms of the number of synapses, cell types, and brain areas analyzed. (See the paper for further details.)

SegCLR automated analysis of brain connectivity. Top: An example mouse pyramidal cell, with synapse locations color-coded according to whether the synaptic partner was classified as inhibitory (blue), excitatory (red), or unknown (black). Inset shows higher detail of the soma and proximal dendrites. Bottom: We counted how many upstream synaptic partners were classified as thalamocortical axons, which bring input from sensory systems to the cortex. We found that thalamic input arrives primarily at cortical layer L4, the canonical cortical input layer, and preferentially targets primary visual area V1, rather than higher visual areas (HVA).

What’s Next?

SegCLR captures rich cellular features and can greatly simplify downstream analyses compared to working directly with raw image and segmentation data. We are excited to see what the community can discover using the ~8 billion embeddings we are releasing for the human and mouse cortical datasets (example access code; browsable human and mouse views in Neuroglancer). By reducing complex microscopy data to rich and compact embedding representations, SegCLR opens many novel avenues for biological insight, and may serve as a link to complementary modalities for high-dimensional characterization at the cellular and subcellular levels, such as spatially-resolved transcriptomics.

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Posted by Shunyu Yao, Student Researcher, and Yuan Cao, Research Scientist, Google Research, Brain Team <!––>

Recent advances have expanded the applicability of language models (LM) to downstream tasks. On one hand, existing language models that are properly prompted, via chain-of-thought, demonstrate emergent capabilities that carry out self-conditioned reasoning traces to derive answers from questions, excelling at various arithmetic, commonsense, and symbolic reasoning tasks. However, with chain-of-thought prompting, a model is not grounded in the external world and uses its own internal representations to generate reasoning traces, limiting its ability to reactively explore and reason or update its knowledge. On the other hand, recent work uses pre-trained language models for planning and acting in various interactive environments (e.g., text games, web navigation, embodied tasks, robotics), with a focus on mapping text contexts to text actions via the language model’s internal knowledge. However, they do not reason abstractly about high-level goals or maintain a working memory to support acting over long horizons.

In “ReAct: Synergizing Reasoning and Acting in Language Models”, we propose a general paradigm that combines reasoning and acting advances to enable language models to solve various language reasoning and decision making tasks. We demonstrate that the Reason+Act (ReAct) paradigm systematically outperforms reasoning and acting only paradigms, when prompting bigger language models and fine-tuning smaller language models. The tight integration of reasoning and acting also presents human-aligned task-solving trajectories that improve interpretability, diagnosability, and controllability..

Model Overview

ReAct enables language models to generate both verbal reasoning traces and text actions in an interleaved manner. While actions lead to observation feedback from an external environment (“Env” in the figure below), reasoning traces do not affect the external environment. Instead, they affect the internal state of the model by reasoning over the context and updating it with useful information to support future reasoning and acting.

Previous methods prompt language models (LM) to either generate self-conditioned reasoning traces or task-specific actions. We propose ReAct, a new paradigm that combines reasoning and acting advances in language models.

ReAct Prompting

We focus on the setup where a frozen language model, PaLM-540B, is prompted with few-shot in-context examples to generate both domain-specific actions (e.g., “search” in question answering, and “go to” in room navigation), and free-form language reasoning traces (e.g., “Now I need to find a cup, and put it on the table”) for task solving.

For tasks where reasoning is of primary importance, we alternate the generation of reasoning traces and actions so that the task-solving trajectory consists of multiple reasoning-action-observation steps. In contrast, for decision making tasks that potentially involve a large number of actions, reasoning traces only need to appear sparsely in the most relevant positions of a trajectory, so we write prompts with sparse reasoning and let the language model decide the asynchronous occurrence of reasoning traces and actions for itself.

As shown below, there are various types of useful reasoning traces, e.g., decomposing task goals to create action plans, injecting commonsense knowledge relevant to task solving, extracting important parts from observations, tracking task progress while maintaining plan execution, handling exceptions by adjusting action plans, and so on.

The synergy between reasoning and acting allows the model to perform dynamic reasoning to create, maintain, and adjust high-level plans for acting (reason to act), while also interacting with the external environments (e.g., Wikipedia) to incorporate additional information into reasoning (act to reason).

ReAct Fine-tuning

We also explore fine-tuning smaller language models using ReAct-format trajectories. To reduce the need for large-scale human annotation, we use the ReAct prompted PaLM-540B model to generate trajectories, and use trajectories with task success to fine-tune smaller language models (PaLM-8/62B).

Comparison of four prompting methods, (a) Standard, (b) Chain of thought (CoT, Reason Only), (c) Act-only, and (d) ReAct, solving a HotpotQA question. In-context examples are omitted, and only the task trajectory is shown. ReAct is able to retrieve information to support reasoning, while also using reasoning to target what to retrieve next, demonstrating a synergy of reasoning and acting.

Results

We conduct empirical evaluations of ReAct and state-of-the-art baselines across four different benchmarks: question answering (HotPotQA), fact verification (Fever), text-based game (ALFWorld), and web page navigation (WebShop). For HotPotQA and Fever, with access to a Wikipedia API with which the model can interact, ReAct outperforms vanilla action generation models while being competitive with chain of thought reasoning (CoT) performance. The approach with the best results is a combination of ReAct and CoT that uses both internal knowledge and externally obtained information during reasoning.

On ALFWorld and WebShop, ReAct with both one-shot and two-shot prompting outperforms imitation and reinforcement learning methods trained with ~105 task instances, with an absolute improvement of 34% and 10% in success rates, respectively, over existing baselines.

Scaling results for prompting and fine-tuning on HotPotQA with ReAct and different baselines. ReAct consistently achieves best fine-tuning performances.

We also explore human-in-the-loop interactions with ReAct by allowing a human inspector to edit ReAct’s reasoning traces. We demonstrate that by simply replacing a hallucinating sentence with inspector hints, ReAct can change its behavior to align with inspector edits and successfully complete a task. Solving tasks becomes significantly easier when using ReAct as it only requires the manual editing of a few thoughts, which enables new forms of human-machine collaboration.

A human-in-the-loop behavior correction example with ReAct on AlfWorld. (a) ReAct trajectory fails due to a hallucinating reasoning trace (Act 17). (b) A human inspector edits two reasoning traces (Act 17, 23), ReAct then produces desirable reasoning traces and actions to complete the task.

Conclusion

We present ReAct, a simple yet effective method for synergizing reasoning and acting in language models. Through various experiments that focus on multi-hop question-answering, fact checking, and interactive decision-making tasks, we show that ReAct leads to superior performance with interpretable decision traces.

ReAct demonstrates the feasibility of jointly modeling thought, actions and feedback from the environment within a language model, making it a versatile agent that is capable of solving tasks that require interactions with the environment. We plan to further extend this line of research and leverage the strong potential of the language model for tackling broader embodied tasks, via approaches like massive multitask training and coupling ReAct with equally strong reward models.

Acknowledgements

We would like to thank Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran and Karthik Narasimhan for their great contribution in this work. We would also like to thank Google’s Brain team and the Princeton NLP Group for their joint support and feedback, including project scoping, advising and insightful discussions.

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Posted by Noah Snavely and Zhengqi Li, Research Scientists, Google Research

We live in a world of great natural beauty — of majestic mountains, dramatic seascapes, and serene forests. Imagine seeing this beauty as a bird does, flying past richly detailed, three-dimensional landscapes. Can computers learn to synthesize this kind of visual experience? Such a capability would allow for new kinds of content for games and virtual reality experiences: for instance, relaxing within an immersive flythrough of an infinite nature scene. But existing methods that synthesize new views from images tend to allow for only limited camera motion.

In a research effort we call Infinite Nature, we show that computers can learn to generate such rich 3D experiences simply by viewing nature videos and photographs. Our latest work on this theme, InfiniteNature-Zero (presented at ECCV 2022) can produce high-resolution, high-quality flythroughs starting from a single seed image, using a system trained only on still photographs, a breakthrough capability not seen before. We call the underlying research problem perpetual view generation: given a single input view of a scene, how can we synthesize a photorealistic set of output views corresponding to an arbitrarily long, user-controlled 3D path through that scene? Perpetual view generation is very challenging because the system must generate new content on the other side of large landmarks (e.g., mountains), and render that new content with high realism and in high resolution.

Background: Learning 3D Flythroughs from Videos

To establish the basics of how such a system could work, we’ll describe our first version, “Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image” (presented at ICCV 2021). In that work we explored a “learn from video” approach, where we collected a set of online videos captured from drones flying along coastlines, with the idea that we could learn to synthesize new flythroughs that resemble these real videos. This set of online videos is called the Aerial Coastline Imagery Dataset (ACID). In order to learn how to synthesize scenes that respond dynamically to any desired 3D camera path, however, we couldn’t simply treat these videos as raw collections of pixels; we also had to compute their underlying 3D geometry, including the camera position at each frame.

The basic idea is that we learn to generate flythroughs step-by-step. Given a starting view, like the first image in the figure below, we first compute a depth map using single-image depth prediction methods. We then use that depth map to render the image forward to a new camera viewpoint, shown in the middle, resulting in a new image and depth map from that new viewpoint.

However, this intermediate image has some problems — it has holes where we can see behind objects into regions that weren’t visible in the starting image. It is also blurry, because we are now closer to objects, but are stretching the pixels from the previous frame to render these now-larger objects.

To handle these problems, we learn a neural image refinement network that takes this low-quality intermediate image and outputs a complete, high-quality image and corresponding depth map. These steps can then be repeated, with this synthesized image as the new starting point. Because we refine both the image and the depth map, this process can be iterated as many times as desired — the system automatically learns to generate new scenery, like mountains, islands, and oceans, as the camera moves further into the scene.

Our Infinite Nature methods take an input view and its corresponding depth map (left). Using this depth map, the system renders the input image to a new desired viewpoint (center). This intermediate image has problems, such as missing pixels revealed behind foreground content (shown in magenta). We learn a deep network that refines this image to produce a new high-quality image (right). This process can be repeated to produce a long trajectory of views. We thus call this approach “render-refine-repeat”.

We train this render-refine-repeat synthesis approach using the ACID dataset. In particular, we sample a video from the dataset and then a frame from that video. We then use this method to render several new views moving into the scene along the same camera trajectory as the ground truth video, as shown in the figure below, and compare these rendered frames to the corresponding ground truth video frames to derive a training signal. We also include an adversarial setup that tries to distinguish synthesized frames from real images, encouraging the generated imagery to appear more realistic.

Infinite Nature can synthesize views corresponding to any camera trajectory. During training, we run our system for T steps to generate T views along a camera trajectory calculated from a training video sequence, then compare the resulting synthesized views to the ground truth ones. In the figure, each camera viewpoint is generated from the previous one by performing a warp operation R, followed by the neural refinement operation gθ.

The resulting system can generate compelling flythroughs, as featured on the project webpage, along with a “flight simulator” Colab demo. Unlike prior methods on video synthesis, this method allows the user to interactively control the camera and can generate much longer camera paths.

InfiniteNature-Zero: Learning Flythroughs from Still Photos

One problem with this first approach is that video is difficult to work with as training data. High-quality video with the right kind of camera motion is challenging to find, and the aesthetic quality of an individual video frame generally cannot compare to that of an intentionally captured nature photograph. Therefore, in “InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images”, we build on the render-refine-repeat strategy above, but devise a way to learn perpetual view synthesis from collections of still photos — no videos needed. We call this method InfiniteNature-Zero because it learns from “zero” videos. At first, this might seem like an impossible task — how can we train a model to generate video flythroughs of scenes when all it’s ever seen are isolated photos?

To solve this problem, we had the key insight that if we take an image and render a camera path that forms a cycle — that is, where the path loops back such that the last image is from the same viewpoint as the first — then we know that the last synthesized image along this path should be the same as the input image. Such cycle consistency provides a training constraint that helps the model learn to fill in missing regions and increase image resolution during each step of view generation.

However, training with these camera cycles is insufficient for generating long and stable view sequences, so as in our original work, we include an adversarial strategy that considers long, non-cyclic camera paths, like the one shown in the figure above. In particular, if we render T frames from a starting frame, we optimize our render-refine-repeat model such that a discriminator network can’t tell which was the starting frame and which was the final synthesized frame. Finally, we add a component trained to generate high-quality sky regions to increase the perceived realism of the results.

With these insights, we trained InfiniteNature-Zero on collections of landscape photos, which are available in large quantities online. Several resulting videos are shown below — these demonstrate beautiful, diverse natural scenery that can be explored along arbitrarily long camera paths. Compared to our prior work — and to prior video synthesis methods — these results exhibit significant improvements in quality and diversity of content (details available in the paper).

Conclusion

There are a number of exciting future directions for this work. For instance, our methods currently synthesize scene content based only on the previous frame and its depth map; there is no persistent underlying 3D representation. Our work points towards future algorithms that can generate complete, photorealistic, and consistent 3D worlds.

Acknowledgements

Infinite Nature and InfiniteNature-Zero are the result of a collaboration between researchers at Google Research, UC Berkeley, and Cornell University. The key contributors to the work represented in this post include Angjoo Kanazawa, Andrew Liu, Richard Tucker, Zhengqi Li, Noah Snavely, Qianqian Wang, Varun Jampani, and Ameesh Makadia.

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Posted by Rishabh Agarwal, Senior Research Scientist, and Max Schwarzer, Student Researcher, Google Research, Brain Team

Reinforcement learning (RL) is an area of machine learning that focuses on training intelligent agents using related experiences so they can learn to solve decision making tasks, such as playing video games, flying stratospheric balloons, and designing hardware chips. Due to the generality of RL, the prevalent trend in RL research is to develop agents that can efficiently learn tabula rasa, that is, from scratch without using previously learned knowledge about the problem. However, in practice, tabula rasa RL systems are typically the exception rather than the norm for solving large-scale RL problems. Large-scale RL systems, such as OpenAI Five, which achieves human-level performance on Dota 2, undergo multiple design changes (e.g., algorithmic or architectural changes) during their developmental cycle. This modification process can last months and necessitates incorporating such changes without re-training from scratch, which would be prohibitively expensive. 

Furthermore, the inefficiency of tabula rasa RL research can exclude many researchers from tackling computationally-demanding problems. For example, the quintessential benchmark of training a deep RL agent on 50+ Atari 2600 games in ALE for 200M frames (the standard protocol) requires 1,000+ GPU days. As deep RL moves towards more complex and challenging problems, the computational barrier to entry in RL research will likely become even higher.

To address the inefficiencies of tabula rasa RL, we present “Reincarnating Reinforcement Learning: Reusing Prior Computation To Accelerate Progress” at NeurIPS 2022. Here, we propose an alternative approach to RL research, where prior computational work, such as learned models, policies, logged data, etc., is reused or transferred between design iterations of an RL agent or from one agent to another. While some sub-areas of RL leverage prior computation, most RL agents are still largely trained from scratch. Until now, there has been no broader effort to leverage prior computational work for the training workflow in RL research. We have also released our code and trained agents to enable researchers to build on this work.

Tabula rasa RL vs. Reincarnating RL (RRL). While tabula rasa RL focuses on learning from scratch, RRL is based on the premise of reusing prior computational work (e.g., prior learned agents) when training new agents or improving existing agents, even in the same environment. In RRL, new agents need not be trained from scratch, except for initial forays into new problems.

Why Reincarnating RL?

Reincarnating RL (RRL) is a more compute and sample-efficient workflow than training from scratch. RRL can democratize research by allowing the broader community to tackle complex RL problems without requiring excessive computational resources. Furthermore, RRL can enable a benchmarking paradigm where researchers continually improve and update existing trained agents, especially on problems where improving performance has real-world impact, such as balloon navigation or chip design. Finally, real-world RL use cases will likely be in scenarios where prior computational work is available (e.g., existing deployed RL policies).

RRL as an alternative research workflow. Imagine a researcher who has trained an agent A1 for some time, but now wants to experiment with better architectures or algorithms. While the tabula rasa workflow requires retraining another agent from scratch, RRL provides the more viable option of transferring the existing agent A1 to another agent and training this agent further, or simply fine-tuning A1.

While there have been some ad hoc large-scale reincarnation efforts with limited applicability, e.g., model surgery in Dota2, policy distillation in Rubik’s cube, PBT in AlphaStar, RL fine-tuning a behavior-cloned policy in AlphaGo / Minecraft, RRL has not been studied as a research problem in its own right. To this end, we argue for developing general-purpose RRL approaches as opposed to prior ad-hoc solutions.

Case Study: Policy to Value Reincarnating RL

Different RRL problems can be instantiated depending on the kind of prior computational work provided. As a step towards developing broadly applicable RRL approaches, we present a case study on the setting of Policy to Value reincarnating RL (PVRL) for efficiently transferring an existing sub-optimal policy (teacher) to a standalone value-based RL agent (student). While a policy directly maps a given environment state (e.g., a game screen in Atari) to an action, value-based agents estimate the effectiveness of an action at a given state in terms of achievable future rewards, which allows them to learn from previously collected data.

For a PVRL algorithm to be broadly useful, it should satisfy the following requirements:

• Teacher Agnostic: The student shouldn’t be constrained by the existing teacher policy’s architecture or training algorithm.
• Weaning off the teacher: It is undesirable to maintain dependency on past suboptimal teachers for successive reincarnations.
• Compute / Sample Efficient: Reincarnation is only useful if it is cheaper than training from scratch.

Given the PVRL algorithm requirements, we evaluate whether existing approaches, designed with closely related goals, will suffice. We find that such approaches either result in small improvements over tabula rasa RL or degrade in performance when weaning off the teacher.

To address these limitations, we introduce a simple method, QDagger, in which the agent distills knowledge from the suboptimal teacher via an imitation algorithm while simultaneously using its environment interactions for RL. We start with a deep Q-network (DQN) agent trained for 400M environment frames (a week of single-GPU training) and use it as the teacher for reincarnating student agents trained on only 10M frames (a few hours of training), where the teacher is weaned off over the first 6M frames. For benchmark evaluation, we report the interquartile mean (IQM) metric from the RLiable library. As shown below for the PVRL setting on Atari games, we find that the QDagger RRL method outperforms prior approaches.

Benchmarking PVRL algorithms on Atari, with teacher-normalized scores aggregated across 10 games. Tabula rasa DQN (–·–) obtains a normalized score of 0.4. Standard baseline approaches include kickstarting, JSRL, rehearsal, offline RL pre-training and DQfD. Among all methods, only QDagger surpasses teacher performance within 10 million frames and outperforms the teacher in 75% of the games.

Reincarnating RL in Practice

We further examine the RRL approach on the Arcade Learning Environment, a widely used deep RL benchmark. First, we take a Nature DQN agent that uses the RMSProp optimizer and fine-tune it with the Adam optimizer to create a DQN (Adam) agent. While it is possible to train a DQN (Adam) agent from scratch, we demonstrate that fine-tuning Nature DQN with the Adam optimizer matches the from-scratch performance using 40x less data and compute.

Reincarnating DQN (Adam) via Fine-Tuning. The vertical separator corresponds to loading network weights and replay data for fine-tuning. Left: Tabula rasa Nature DQN nearly converges in performance after 200M environment frames. Right: Fine-tuning this Nature DQN agent using a reduced learning rate with the Adam optimizer for 20 million frames obtains similar results to DQN (Adam) trained from scratch for 400M frames.

Given the DQN (Adam) agent as a starting point, fine-tuning is restricted to the 3-layer convolutional architecture. So, we consider a more general reincarnation approach that leverages recent architectural and algorithmic advances without training from scratch. Specifically, we use QDagger to reincarnate another RL agent that uses a more advanced RL algorithm (Rainbow) and a better neural network architecture (Impala-CNN ResNet) from the fine-tuned DQN (Adam) agent.

Reincarnating a different architecture / algorithm via QDagger. The vertical separator is the point at which we apply offline pre-training using QDagger for reincarnation. Left: Fine-tuning DQN with Adam. Right: Comparison of a tabula rasa Impala-CNN Rainbow agent (sky blue) to an Impala-CNN Rainbow agent (pink) trained using QDagger RRL from the fine-tuned DQN (Adam). The reincarnated Impala-CNN Rainbow agent consistently outperforms its scratch counterpart. Note that further fine-tuning DQN (Adam) results in diminishing returns (yellow).

Overall, these results indicate that past research could have been accelerated by incorporating a RRL approach to designing agents, instead of re-training agents from scratch. Our paper also contains results on the Balloon Learning Environment, where we demonstrate that RRL allows us to make progress on the problem of navigating stratospheric balloons using only a few hours of TPU-compute by reusing a distributed RL agent trained on TPUs for more than a month.

Discussion

Fairly comparing reincarnation approaches involves using the exact same computational work and workflow. Furthermore, the research findings in RRL that broadly generalize would be about how effective an algorithm is given access to existing computational work, e.g., we successfully applied QDagger developed using Atari for reincarnation on Balloon Learning Environment. As such, we speculate that research in reincarnating RL can branch out in two directions:

• Standardized benchmarks with open-sourced computational work: Akin to NLP and vision, where typically a small set of pre-trained models are common, research in RRL may also converge to a small set of open-sourced computational work (e.g., pre-trained teacher policies) on a given benchmark.
• Real-world domains: Since obtaining higher performance has real-world impact in some domains, it incentivizes the community to reuse state-of-the-art agents and try to improve their performance.

See our paper for a broader discussion on scientific comparisons, generalizability and reproducibility in RRL. Overall, we hope that this work motivates researchers to release computational work (e.g., model checkpoints) on which others could directly build. In this regard, we have open-sourced our code and trained agents with their final replay buffers. We believe that reincarnating RL can substantially accelerate research progress by building on prior computational work, as opposed to always starting from scratch.

Acknowledgements

This work was done in collaboration with Pablo Samuel Castro, Aaron Courville and Marc Bellemare. We’d like to thank Tom Small for the animated figure used in this post. We are also grateful for feedback by the anonymous NeurIPS reviewers and several members of the Google Research team, DeepMind and Mila.

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