arXiv AI
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
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arXiv AI
Oct 27
EnvLunch: The Environment as a Free Lunch for Learning to Interact
EnvLunch proposes a warm‑up training stage that uses the executable environment itself as a source of verifiable tasks, eliminating the need for human‑written or model‑generated task descriptions. By sampling valid action trajectories, executing them, and training agents to recover equivalent trajectories from final observations, EnvLunch achieves over 9‑point performance gains on downstream tasks and reduces the required annotated examples by 8 to 32 times compared to baselines. This approach also improves fine‑tuning on benchmarks beyond data analysis, offering a more efficient starting point when annotated tasks are scarce.
arXiv Computation and Language
Oct 27
Trajectory2Gym: Decision-Grounded Compositional Interaction Synthesis for Tool-Use Agents
arXiv:2610.33568v1 Announce Type: new Abstract: Successful tool-use trajectories are not necessarily useful supervision. Even successful interactions may underrepresent multi-step dependencies or con...
arXiv Machine Learning
Sep 22
Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents
The paper introduces Trace, a framework that transforms sparse-reward trajectories into executable walkthroughs by identifying progress anchors, propagating credit, and estimating action prerequisites. Trace compiles noisy trajectories into state‑conditioned, verifiable procedures that remove loops and detours, enabling reuse, intermediate‑state resumption, and programmatic verification. Experiments on J‑TTL, WebShop, and ScienceWorld with three open‑source LLMs show that Trace outperforms eight baselines, improving average AUC and Final‑$3$ by 30.0% and 40.5% while using fewer inference tokens.
arXiv Computation and Language
Aug 31
ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL
ContextPilot is a proactive context‑management framework designed to improve long‑horizon agentic reasoning with large language models. It expands the toolset to include planning, long‑term memory, and soft context offloading, and introduces a reinforcement‑learning strategy that focuses on critical editing decisions and assigns action‑level advantages. Experiments on long‑context QA and deep search tasks demonstrate that ContextPilot achieves stronger performance with a more compact working context, outperforming existing baselines across various base models and benchmarks.
arXiv AI
Sep 24
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv AI
Oct 21
GRADUS: Discovering and Recovering Agent Lessons from the Environment's Latent Syllabus
GRADUS introduces a method for training large language model agents in policy-constrained tool environments by decoupling task design from solutions. It treats a fixed environment as a latent syllabus, extracting guarded behavioral operators and composing them into progressively harder tasks while using corpus-level feedback to target missing outcome combinations. The approach jointly calibrates tasks and solutions against the environment, turning verification into a recovery signal that preserves difficult examples, leading to significant performance gains on benchmarks such as $ au^2$-Bench and CAR-Bench.