HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents
Long-horizon agents compress interaction histories into memory, but final outcomes entangle memory-writing credit with tool failures and reasoning errors. We propose HiMPO, a Hindsight-Informed Memory Policy Optimization framework that supplements trajectory-level reinforcement learning with memory-specific credit. For each update, HiMPO compares the target answer's log-likelihood under the previous and updated memories while holding the remaining pre-write context fixed. A soft gate based on group-relative hindsight relevance then weights the local utility advantage. Trajectory-level credit applies to all generated tokens; memory tokens additionally receive the auxiliary advantage. Acros...
| Authors | Jiangze Yan, Yi Shen, Wenjing Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 14 October 2026 |
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