Reinforcement Learning via Self-Distillation — mentioned by user7635

Mon, 09 Feb 2026 23:34:59 GMT

Came across a recent paper, “Reinforcement Learning via Self-Distillation,” introducing Self-Distillation Policy Optimization (SDPO). The idea is to use rich feedback (e.g., textual/runtime signals) to create a “self-teacher” distribution and distill it back into the policy, giving dense per-token credit assignment instead of relying purely on sparse scalar rewards. Claims improved sample efficiency and better performance on reasoning / programming tasks. Paper: https://arxiv.org/abs/2601.20802

Paper and discussion history. A mention is not an endorsement.

Reinforcement Learning via Self-Distillation · HackerPapers discussions

Reinforcement Learning via Self-Distillation — mentioned by user7635