Cursor Team Releases Report on Stabilizing RL + Self-Distillation in Agentic Training
kvfrans · x · 2026-11-07
Cursor researcher Kevin Frans, with Alexander Rush, Sergey Levine, Pieter Abbeel and Ashvin Nair, released an arXiv paper on stabilizing RL + self-distillation in production-like agentic settings.
- Proposes targeted guidance: per-turn textual feedback (e.g. rubric-based judge models) combined with an on-policy self-distillation objective to optimize hard-to-improve rewards.
- Key finding: combining targeted self-distillation with per-criteria RL rewards, and limiting feedback to turns where the policy fails, works stably in multi-turn software engineering environments.
- Also examines design choices that keep entropy stable and reduce oscillatory behavior during training.
- Author notes the setup is somewhat stale but useful as a pitfall guide.
Related event: Cursor and UC Berkeley Use RL to Teach Models New Behaviors from Scratch(2 posts)→
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