arXiv AI August 11, 2026 By Yubo Jiang, Fengying Xie, Zhiguo Jiang, Haopeng Zhang

Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation

arXiv:2608. 09826v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards yields no group-relative signal when rollout groups are uniformly correct or uniformly wrong, which account for 63.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Hugging Face Trending Papers

Aug 10

Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation

Reinforcement learning with verifiable rewards yields no group-relative signal when rollout groups are uniformly correct or uniformly wrong, which account for 63. 0-68.

reinforcement-learning efficiency benchmarks

arXiv AI

Aug 26

Skill-Conditioned Gated Self-Distillation for LLM Reasoning

arXiv:2605.28791v2 Announce Type: replace-cross Abstract: On-policy self-distillation (SD) improves LLM reasoning by using teacher-side privileged information (PI) to turn sparse verifier outcomes in...

By Jiazhen Huang, Xiao Chen, Xiao Luo, Yong Dai, Senkang Hu, Yuzhi Zhao

llms efficiency benchmarks

arXiv AI

Aug 6

Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation

arXiv:2608. 04794v1 Announce Type: new Abstract: Self-distillation (SD) has emerged as a compute-efficient alternative to reinforcement learning with verifiable rewards: a self-teacher, conditioned on privileged information (PI) about the answer such as a reference solution, supplies dense per-token supervision to a student that never sees it.

By Sarthak Harne, Chinmay Karkar, Yash Pandya, Ahmed Awadallah, Akshay Nambi

agents nlp reinforcement-learning efficiency safety

arXiv Machine Learning

Aug 14

I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization

arXiv:2608. 12957v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) learns from reward differences within a rollout group, but receives no useful relative signal when every sampled response is incorrect.

By Yubo Zhang, Xinhong Ma, Zezhong Tan, Ziqiang Dong

efficiency safety

arXiv Machine Learning

Sep 1

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

The paper investigates on‑policy distillation (OPD), showing that teacher supervision during OPD contains significant noise that grows with teacher size, yet the student policy remains largely unaffected by this noise. It finds that OPD’s gains stem mainly from suppressing low‑log‑probability tokens, a process that can be replicated without a teacher. Building on this insight, the authors propose On‑Policy Self‑Adaptation (OPSA), a supervision‑free method that uses entropy‑adaptive negative advantages to improve performance on several benchmarks, outperforming both the base model and OPD.

By Yi Ding, Ruqi Zhang

reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 30

Weak-to-Strong On-Policy Distillation

arXiv:2607. 26246v1 Announce Type: new Abstract: On-policy distillation (OPD), which aligns a student with the teacher's token-level distribution on the student's own rollouts, is an effective paradigm for transferring capabilities across LLMs.

By Fangxu Yu, Zinan Lin, Xiaodong Liu, Weijia Xu, Michael Xu, Tianyi Zhou, Jianfeng Gao

llms reinforcement-learning efficiency benchmarks

One email a morning, machine-written

Related stories

Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation

Skill-Conditioned Gated Self-Distillation for LLM Reasoning

Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation

I-SDPO: Instance-Level Adaptive Self-Distillation Policy Optimization

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

Weak-to-Strong On-Policy Distillation