ReTRAIL: Rewinding Simulations While Carrying Forward Learned Experience for Long‑Horizon Agents

Long‑horizon agents — planners, embodied agents, interactive assistants — frequently suffer when an early mistake produces a cascade of failures or a dead‑end trajectory. A natural idea is to let the agent rewind the environment and try a different branch. The arXiv preprint introduces ReTRAIL, a framework that pairs environment rewinding with mechanisms to preserve and reuse knowledge from abandoned exploration so the agent actually benefits from restarting. What the paper reports ReTRAIL makes two core moves. First, it permits the environment state to be reset or rewound to prior points while the agent’s internal knowledge (policies, value estimates) continues to evolve — the environment moves backward and the agent’s knowledge moves forward. Second, it trains a single shared policy using two complementary learning signals: State‑Anchored GRPO, which compares alternative continuations from revisited states to improve execution choices, and a branch‑advancement reward that encourages the agent to reflect on later progress achieved after rewinding. The authors evaluate ReTRAIL on four interactive benchmarks: MineSweeper, Sokoban, ALFWorld, and ScienceWorld. They report that a naive rewind primitive alone can provide little benefit and even hurt performance, whereas coupling rewinds with experience reuse via ReTRAIL helps agents escape unproductive states and guide subsequent exploration more effectively. Code is available at https://github.com/whi497/ReTRAIL. Why this matters for builders