Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents

Key points

• Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dead-end trajectories.1
• The environment state moves backward while the agent's knowledge continues to move forward.1
• ReTRAIL trains a shared policy with two complementary signals: State-Anchored GRPO compares alternative continuations from revisited states to improve execution, while a branch-advancement reward guides reflection through the progress of subsequent exploration.1
• These results demonstrate the value of coupling environment rewinding with experience reuse within an ongoing task for long-horizon task solving.1

Sources (1)

• [1]Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 06:11 AM

  We introduce ReTRAIL, a framework that enables agents to rewind the environment to earlier states while preserving valid progress and carrying forward knowledge learned from abandoned exploration.

  Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dead-end trajectories.

Extractive summary: sentences quoted from the sources.