MemOPD: On-Policy Distillation through Memory State Alignment for Long-Horizon Agents
Long-horizon agents accumulate growing contexts during interaction, impairing performance and stability. Compact memory mitigates this problem by compressing and rewriting the history retained between model invocations. Learning what to retain typically relies on proximal policy optimization (PPO) with final task rewards, but sparse rewards provide little guidance for individual memory updates. This limitation motivates on-policy distillation (OPD), which supplies dense teacher supervision on st
Record details
Published: 7 August 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Agents & autonomy
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “MemOPD: On-Policy Distillation through Memory State Alignment for Long-Horizon Agents,” evidence record 17809, https://ethics.ai/record/17809 (originally published by arXiv).
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