Grounds language-based robot planning in learned skill value functions, selecting actions that are both useful and feasible. PaLM-SayCan reports 84% planning and 74% execution success across 101 real-world instructions.

Breaks hard reasoning problems into simpler subproblems, then solves them using earlier answers. With 14 exemplars, code-davinci-002 reaches at least 99% accuracy on SCAN splits, including the length split.

Turns language commands into executable robot policies that combine perception, control APIs, loops, and geometry libraries. Hierarchical code generation recursively fills in missing functions to support new behaviors without policy retraining.

Splits complex tasks into reusable subtasks, each with its own prompt or symbolic handler. Recursive decomposition and retrieval improve GPT-3 reasoning on symbolic tasks and multi-hop question answering.

ReAct: Synergizing Reasoning and Acting in Language Models

Interleaves chain-of-thought reasoning with tool-using actions in one LLM loop: the model plans, queries a source like Wikipedia, then revises from results. Cuts hallucination versus reasoning-only prompting and beats trained agents on interactive tasks.

Teaches a language model when and how to call external APIs using a few demonstrations and self-generated training data. Calls are retained only when their results improve prediction of subsequent text.

Tests whether browser agents complete 812 tasks across realistic shopping, forum, development, and content-management sites. Evaluates final outcomes rather than matching a reference sequence of clicks.

Aligns language models by generating candidate outputs, filtering them with a reward model, and reusing the retained data for repeated fine-tuning. Translation experiments test this alternative to continual online sampling.

Lets language models move information between their context window and external storage. Memory editing, retrieval, and function chaining support conversations and document analysis beyond a single prompt's capacity.

Improves language-model problem solving by generating candidate answers, retaining verified successes, and fine-tuning on them. Repeats this loop with an expectation-maximization interpretation instead of collecting more human-written solutions.

Tests whether web agents can use visual evidence to finish 910 tasks across shopping, classifieds, and social forums. Execution-based checks distinguish successful actions from plausible explanations.

Lets language-model agents compose tools through executable Python and revise actions from interpreter feedback. Evaluates 17 models and releases 7,000 interaction trajectories for training agents that can debug their own code.