Hugging Face Trending Papers July 30, 2026

Contrastive Reinforced Policy Optimization via Privileged Self-Distillation

Read the original on Hugging Face Trending Papers →

Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD). While OPSD provides dense, logit-level supervision, it inherently suffers from exposure bias due to the privileged information of the self-teacher.

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 Hugging Face Trending Papers.

arXiv Machine Learning

Jul 31

Contrastive Reinforced Policy Optimization via Privileged Self-Distillation

arXiv:2607. 28026v1 Announce Type: new Abstract: Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD).

By Xingjian Wu, Junlin Liu, Xingchen Liu, Xuhang Zhu, Jianing Wang, Linsen Guo, Xiaoyu Li, Xuezhi Cao, Xunliang Cai

llms agents reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Jul 31

Group-Reflective Self-Distillation for Agentic Reinforcement Learning

arXiv:2607. 28076v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents.

By Binbin Zheng, Zijun Xie, Guanqun Zhao, Enlei Gong, Xing Ma, Xiaoliang Fu, Zeyu Chen

llms agents reinforcement-learning efficiency

arXiv AI

Jul 17

Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization

arXiv:2607. 14614v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) commonly uses entropy for advantage shaping.

By Weiwen Xu, Jia Liu, Hou Pong Chan, Long Li, Deng Cai, Min Chen, Hao Zhang

reinforcement-learning efficiency benchmarks

arXiv AI

4d ago

SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation

SIPO (Self‑Instructing Policy Optimization) unifies reinforcement learning with on‑policy self‑distillation by using a contrastive self‑teacher to generate token‑level credit signals. The method samples multiple rollouts per prompt, pairs each with a reference answer and its mistakes, and uses the difference in teacher log‑probabilities to provide dense feedback while still respecting the overall task reward. Experiments on reasoning and code‑generation benchmarks show that SIPO outperforms both RLVR and OPSD baselines without requiring an external teacher or extra generation steps.

By Zhenrui Yue, Huimin Zeng, Yueqi Wang, Yaokun Liu, Fengran Mo, Jinghan Zhang, Mung Yao Jia, Gyuseok Lee, Yang Zhang, Na Wei, Dong Wang

llms reinforcement-learning efficiency benchmarks

arXiv AI

Aug 25

Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents

arXiv:2608.21830v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) have shown strong potential for automating tasks across diver...

By Chengyang Gu, Le Zhang, Jingbo Zhou, Yize Chen, Yu Shi, Siqi Bao, Zheng-Fan Wu, Hua Wu, Hui Xiong

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Sep 1

OISD: On-Policy Internal Self-Distillation of Language Models

arXiv:2605.29089v2 Announce Type: replace-cross Abstract: Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, wh...

By Xinyu Liu, Darryl Cherian Jacob, Yang Zhou, Jindong Wang, Pan He

llms reinforcement-learning efficiency safety

One email a morning, machine-written

Related stories

Contrastive Reinforced Policy Optimization via Privileged Self-Distillation

Group-Reflective Self-Distillation for Agentic Reinforcement Learning

Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization

SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation

Beyond Success and Failure: Length-Aware Contrastive Learning for GUI Agents

OISD: On-Policy Internal Self-Distillation of Language Models