PaperlayerView exact revision on arXiv

Source-linked AI summary

A Generalist Agent

Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, Nando de Freitas

arXiv:2205.06175v3cs.AIcs.CLcs.LGcs.RO

TL;DR

The paper addresses whether one agent can achieve broad capability across tasks, modalities, and embodiments rather than relying on specialized policies. It presents Gato, a single transformer trained on diverse data, and reports broad competence across control, dialogue, vision, and robotics, including adaptation to new tasks. The authors conclude that such transformer policies show promise as general starting points for learning new behaviors, while emphasizing deployment and alignment risks.

  • Problem

    The paper asks whether a generally capable agent can operate across many tasks and embodiments and adapt to additional tasks with little extra data.

  • Method

    Gato is a single large transformer sequence model trained across multimodal, multitask, and multi-embodiment data, with fine-tuning used for some new tasks.

  • Results

    Gato performs across dialogue, image captioning, Atari, simulated environments, and real-robot block stacking, while exceeding 50% of expert score on over 450 of 604 simulated control tasks.

  • Takeaways & Limitations

    Transformer sequence models show promise as general-purpose policies that can serve as starting points for learning new behaviors through prompting or fine-tuning.

  • Takeaways & Limitations

    Generalist agents can create physical-world safety risks, including misplaced trust, harmful cross-domain behavior transfer, and societal impacts under limited oversight.

Abstract

from arXiv · show

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

1 Introduction

Gato is a single general-purpose agent designed to operate across many tasks, modalities, and embodiments. The paper tests whether broad capability can be achieved and extended through scaling and limited adaptation.

  • A single sequence model reduces hand-crafted domain-specific policies and can ingest more diverse training data when information is serialized into flat sequences.The shared representation supports data from different domains and modalities.
  • Gato uses one large transformer with one set of weights for dialogue, image captioning, robotics, Atari, 3D navigation, instruction following, and more.The same general-purpose agent spans text, vision, games, simulated environments, and real-world manipulation.
  • The paper tests whether an agent can be generally capable across many tasks and adapt to additional tasks with little extra data.This is presented as a hypothesis rather than a claim that every task can be mastered.
  • Gato is trained offline with supervised learning at approximately 1.2B parameters, an operating point chosen to permit real-time control of real-world robots.The authors note that reinforcement learning could also be used in principle.

2 Model

Gato converts multimodal observations, actions, and text into token sequences and models them autoregressively with a decoder-only transformer. During deployment, it samples action tokens conditioned on the preceding context.

  • Tokenization: Gato serializes images, text, proprioception, joint torques, button presses, and other observations or actions into flat token sequences.The same sequence-modeling interface is used across modalities and task types.
  • Tokenization: Continuous values are mu-law encoded when needed, discretized into 1024 bins, and shifted into the integer range [32000, 33024).Discrete values such as Atari button presses are represented as integers in [0, 1024).
  • Tokenization: Text, images, tensors, and agent timesteps each follow canonical token and sequence orderings before entering the model.Text preserves input order, image patches use raster order, tensors use row-major order, and episodes proceed chronologically.
  • Embedding and targets: A parameterized embedding function maps tokens to vectors, using lookup embeddings for text and non-image values and a ResNet block for image patches.Learnable position encodings are added according to the token’s local timestep or within-image position.
  • Training: Gato predicts text, discrete and continuous values, and actions autoregressively, while masking image and nontextual observation tokens out of the loss.The masking function assigns training weight to text and logged action tokens.
  • Training and deployment: The model is a 1.2B-parameter, 24-layer decoder-only transformer trained on mixed-domain subsequences, with prompt conditioning used to disambiguate some tasks.During control, observations and prior actions are appended to context and the action vector is sampled token by token.

3 Datasets

Gato is trained on heterogeneous control, language, image, and robotics datasets collected from simulated and real environments. The data include specialist-agent experience, natural-language corpora, vision-language pairs, and RGB-stacking demonstrations.

  • Dataset mixture: Gato’s training mixture includes agent experience from simulated and real-world environments together with natural-language and image datasets.The datasets and their attributes are summarized in Table 1.
  • Control datasets: Control data come from specialist reinforcement-learning agents, with recorded states, actions, and rewards from environments including Meta-World, Sokoban, BabyAI, DM Control, DM Lab, Atari, Procgen, and robotic arms.These sources cover planning, instruction following, continuous control, navigation, vision, games, and manipulation.
  • Control datasets: Training episodes are filtered to retain returns at least 80% of the task’s expert return.The expert return is defined from windowed average returns over collected episodes.
  • Vision and language datasets: Language and vision-language data include MassiveText, ALIGN’s 1.8B image-alt-text pairs, LTIP’s 312M captioned images, and additional captioning and webpage datasets.These datasets provide text, images, captions, and multimodal webpages.
  • Robotics datasets: The robotics data include Sawyer-arm RGB stacking with image, joint-angle, and end-effector observations, plus 387k simulated and 15k real successful trajectories for specified training objects.The benchmark varies block shapes and holds out five shape triplets for testing.

4 Capabilities of the generalist agent

Gato’s single pretrained model demonstrates broad capabilities across simulated control, Atari, robotics, image captioning, and dialogue. Its performance is strong across many control tasks, while language capabilities remain rudimentary.

  • All reported results derive from one pretrained model with a single set of weights, before fine-tuning.
  • Simulated control tasks: Over 450 of 604 simulated control tasks exceed the 50% expert-score threshold.Scores are averaged over 50 rollouts per task; 100% denotes the per-task expert and 0% a random policy.
  • Simulated control tasks: Gato reaches average-human-or-better scores on 23 Atari games and exceeds twice human score on 11.
  • Simulated control tasks: On BabyAI, Gato exceeds 80% of expert score on nearly all levels, scoring 75% on the most difficult BossLevel.
  • Simulated control tasks: Gato exceeds 50% of expert score on 44 of 45 Meta-World tasks and on 21 of 30 state-based DM Control tasks.It exceeds 80% on 35 Meta-World tasks and 18 DM Control tasks, and exceeds 90% on 3 Meta-World tasks.
  • Robotics: On real-robot RGB Stacking, Gato’s success rate on five unseen-shape test triplets is comparable to a published single-task behavioral-cloning baseline.The generalist was evaluated for 200 episodes per test triplet.
  • Language and vision: Gato shows rudimentary image captioning and dialogue abilities; dialogue is usually relevant but often superficial or factually incorrect.The captioning examples use held-out MS-COCO images, while the dialogue examples are hand-picked.

5 Analysis

Gato’s analysis examines scaling, transfer, and task adaptation across control, robotics, and Atari. Larger models and broad pretraining generally improve performance, while transfer remains task-dependent and specialist agents can retain advantages.

  • 5.1 Scaling Laws Analysis: Increasing model scale improves aggregate in-distribution performance at equivalent token counts across domains.The comparison includes 79M, 364M, and 1.18B parameter models, with normalized return averaged first within tasks and domains, then across domains.
  • 5.2 Out of distribution tasks: Pretraining on all datasets gives the best few-shot fine-tuning results on cartpole.swingup and assembly-v2, followed by same-domain pretraining.With no control data, transfer is absent for cartpole.swingup and negative for assembly-v2.
  • 5.2 Out of distribution tasks: Pretraining effects vary by task: DM Lab order_of_apples_forage_simple approaches maximum reward with DM Lab data alone, while boxing shows no pretraining benefit.The boxing result is hypothesized to reflect visual differences that make transfer difficult.
  • 5.3 Robotics: Skill Generalization: 10 episodes of fine-tuning recover the robotics expert’s performance, while 100 or 1000 episodes produce performance above the expert.This pattern appears in both simulation and reality, with slight degradation after 5000 episodes.
  • 5.3 Robotics: Skill Generalization: Larger models adapt more effectively with fewer robotics fine-tuning episodes, although the 364M model overfits on one episode and the 79M model performs worse.The comparison uses 1.18B, 364M, and 79M parameter variants in simulation.
  • 5.3 Robotics: Adaptation to Perceptual Variations: 60% real-robot success on blue-on-green stacking exceeds the 0.5% success of a from-scratch behavior-cloning baseline.Adding simulated blue-on-green demonstrations improved fine-tuning performance, with a 10% sampling ratio identified as ideal.
  • 5.4 Robotics: Skill Mastery: Gato exceeds or closely matches BC-IMP on all but one training triplet in robotics skill mastery.These results use an earlier Gato architecture without fine-tuning.
  • 5.5 Meta-World: The 79M Meta-World agent reaches a 96.6% average success rate across all 50 tasks.The agent distills trajectories from single-task MPO experts into one multi-task agent.

6 Related Work

Gato builds on generic sequence and transformer models while extending them toward a single multi-modal, multi-embodiment agent. Its related work situates this design among generalist, transfer, robotics, and autoregressive modeling approaches, while identifying a gap in large-scale unified agents.

  • Generic sequence models: Decision Transformers and Trajectory Transformer demonstrate generic language-model-like architectures for control, while Gato adds support for multi-modality, multi-embodiment, scale, and general-purpose deployment.The related architectures provide the closest comparison identified in the paper.
  • Multi-embodiment control: Multi-embodiment control work includes graph networks, transformers without morphological inductive biases, and modular policies for multi-task or multi-robot transfer.These approaches address variation in bodies or tasks without being identical to Gato’s design.
  • Earlier generalist models: Earlier generalist systems include NPI, MultiModel, and other models spanning distinct programs, speech, images, and text.These works establish precedents for one model operating across heterogeneous domains or modalities.
  • Shared weights: The paper distinguishes one shared architecture from one network with the same weights across tasks, noting that many multi-task agents use different parameters per task.This distinction defines the stronger form of generalism claimed by Gato.
  • Research gap: The authors state that no previously reported system combined hundreds of vision, language, and control tasks in one modern transformer generalist at scale.This is presented as the reported gap addressed by the paper.
  • Conceptual foundations: Sensory substitution and neuroscience arguments are cited as conceptual support for a shared network processing different input signal types.These examples motivate, rather than empirically establish, a single model for diverse modalities.
  • Transfer limitations: Atari transfer is difficult because games differ substantially in visuals, controls, and strategy, and autoregressive action generation can suffer self-delusion under confounding variables.The paper uses prompt conditioning to reduce self-delusions and leaves counterfactual teaching for future work.
  • Data-driven robotics: Robotics research is constrained by data that are costly to collect and insufficiently diverse across embodiments, tasks, and environments.The paper presents generalist data-driven modeling as a response to this stated robotics data challenge.

7 Broader Impact

Gato’s broad capabilities create risks spanning inherited vision-language concerns, physical action, knowledge transfer, alignment, and interpretability. The authors call for interdisciplinary analysis and further mitigation research before deployment.

  • Transparency and mitigation: The paper documents intended use cases in a model card, while noting that tools for mitigating generalist-agent harms remain underdeveloped.Further research is required before these agents are deployed.
  • Risks of generalist embodiment: Gato’s physical embodiment can create misplaced trust after malfunctions, enable misuse, and transfer behaviors learned in one domain into harmful contexts.The authors identify anthropomorphism, bad actors, and undesired cross-domain behavior transfer as distinct concerns.
  • Alignment and oversight: Generalist agents operating across many embodiments make technical AGI safety more challenging and increase the importance of preference learning, uncertainty modeling, and value alignment.The paper notes that alignment methods for language might be extended to generalist agents, but unforeseen circumstances and limited oversight can still cause harm.
  • Interpretability: Understanding how Gato processes information and develops emergent capabilities requires significant experimentation.The authors suggest external retrieval as a future design direction because it has been shown to improve interpretability and performance.
  • Deployment: The authors are not currently deploying Gato to users, but recommend thoughtful development and deployment because generalist models may have substantial societal impact.They frame Gato as proof-of-concept progress whose risks and benefits warrant attention from safety researchers, ethicists, and the public.

8 Limitations and Future work

The paper identifies data acquisition, task prompting, and limited context as central constraints on extending Gato. It presents offline RL, richer datasets, and more efficient long-context architectures as future directions.

  • Data: A web-scale control dataset is not currently available, making data acquisition a challenge for scaling this imitation-learning-based approach.The authors characterize suitable data acquisition as an independent, active research question.
  • Data: Offline RL, richer simulations, online games, and observation-only video datasets may provide larger or more diverse control data.These sources differ in practicality and quality: simulation can generate high-quality data at scale, whereas web data is often low quality.
  • Prompting: Gato uses an expert demonstration prompt to infer the task when no task identifier is available.The prompt contains observations and actions that aid the agent in producing task-relevant actions.
  • Context length: A 1024-token context can limit image-based episodes to only a few environment timesteps because each image observation may exceed one hundred tokens.Consequently, only a short demonstration chunk may fit in transformer memory for some environments.
  • Context length: Early prompt-structure and in-context-learning experiments showed similar performance across prompt formats and no significant improvement over prompt-less evaluation.These preliminary results were obtained under the limited prompt context.
  • Future architectures: Quadratic self-attention scaling constrains context length, motivating exploration of architectures that support longer contexts more efficiently.The authors suggest that longer context could potentially improve agent performance.

9 Conclusions

The paper concludes that transformer sequence models can serve as multi-task, multi-embodiment policies across text, vision, and robotics. Scaling parameters, data, and compute could improve performance and support prompting or fine-tuning for new behaviors.

  • Conclusions: Transformer sequence models are effective multi-task, multi-embodiment policies for real-world text, vision, and robotics tasks.The conclusion also reports promise for few-shot learning of out-of-distribution tasks.
  • Conclusions: Future agents could use prompting or fine-tuning as a default starting point for learning new behaviors instead of training from scratch.This proposed use follows the reported few-shot and broad-task capabilities.
  • Scaling: Scaling parameters, data, and compute is expected to increase performance across tasks, including dialogue.Better hardware and network architectures could enable larger models while preserving real-time robot control.
  • Scaling: The authors argue that iterating on the same scaled approach could produce a useful general-purpose agent.This conclusion is conditioned on continued scaling and improved hardware and architectures.

A Model card

The model card describes Gato’s evaluation scope, multimodal training data, tokenization and architecture, and evaluation procedures. It also records dataset, physical-safety, and deployment caveats.

  • Evaluation: Gato is evaluated on in-distribution and out-of-distribution simulated control tasks, plus the RGB Stacking Skill Generalization robotics benchmark.The evaluations probe multimodal multitask learning and adaptation to entirely new tasks.
  • Preprocessing and sequencing: Evaluation observations are tokenized into discrete embeddings, while episodes are ordered by timestep with observations, a separator, and actions forming the sequence.Text, image patches, and tensors use modality-specific ordering, and the total sequence length is L = T(k + m + n + 1 + A).
  • Datasets: Training uses diverse datasets from simulated and real-world agent experience together with natural-language and image datasets.Joint training produces one network capable of Atari, image captioning, dialogue, and real-robot block stacking.
  • Risks and mitigations: The model card notes harmful context in vision and language datasets and physical-safety harms from real-world misuse or malfunctioning.Bias mitigation is limited to filtering sexually explicit content, while physical risk is addressed through robotics-environment safety measures.
  • Caveats and future work: The interaction of diverse training domains and evaluation affordances is poorly understood, creating potential ethical and safety risks as capabilities grow.The model card lists this as a future-work caveat.
  • Tokenization: Continuous tensor values are mu-law companded, clipped to [−1, 1], and discretized into 1024 uniform bins shifted into token range [32000, 33024).The stated companding parameters are µ = 100 and M = 256.
  • Architecture: Image patches are converted into token embeddings with a v2 ResNet block using GroupNorm and GELU instead of LayerNorm and RELU.The block architecture is documented in Figure 16 and Table 5 lists transformer hyperparameters.
  • Evaluation procedure: Final fine-tuning performance is the maximum smoothed score, where each checkpoint score averages 10 runs and the moving average combines 5 such scores.This procedure gathers 50 runs into the moving average.

I Additional robotics ablations

The robotics ablations compare Gato with domain-specific baselines and analyze how its transformer attends to image tokens across tasks. The attention analysis finds interpretable, task-specific patterns in early-layer heads.

  • Robotics ablations: The DM Control-only agent outperforms the base Gato in zero-shot transfer and after extensive fine-tuning.The comparison used the 364M-parameter variant and included a control-suite-only baseline.
  • Attention analysis: Attention is represented by cross-attention logits with dimensions (H, T, T), where each entry measures a head’s attention from one token to another.Multiple image tokens per timestep require selecting the corresponding sub-matrix for timestep-level visualization.
  • Attention analysis: For each image patch, attention importance is averaged over the causal sub-column beneath the matrix diagonal.This computes the average attention paid to a patch over a whole timestep.
  • Attention analysis: First-layer attention maps are most interpretable, with selected heads tracking task-specific entities and image regions across Atari, Procgen, robotics, and control tasks.The illustrated tasks include Atari Breakout, Boxing, Pong, Freeway, Procgen CoinRun, Bossfight, RGB Stacking, and DM Control Suite Cheetah.

K Detailed results for specialist Meta-World agent

The specialist Meta-World agent is evaluated across all 50 tasks, while the surrounding results report normalized Gato scores by domain. The specialist evaluation averages performance over repeated trials.

  • Specialist Meta-World results: 96.6% success rate is achieved by the specialist Meta-World agent averaged across all 50 Meta-World tasks.The agent was evaluated 500 times for each task.
  • Evaluation protocol: 500 evaluations per task are used to calculate the specialist Meta-World success rates.The detailed task-level success rates are presented in Table 7.
  • Gato simulated-control results: Normalized Gato scores are reported separately for each simulated-control domain, with 50 evaluations per task.These results are presented in Table 8.
Loading 2205.06175v3…