Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal. However, the richer procedural information in the rollout is rarely retained or reused. Across episodes and epochs, the model repeatedly encounters related problems under a changing policy, producing cross-episode signals that episode-local updates cannot capture: which strategies consistently pass verification, which failure modes persist, which patterns recur. We propose Procedural Memory Distillation (PMD), which converts these crossepisode signals into reusable procedural memory...
| Authors | Ye Liu, Srijan Bansal, Bo Pang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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