:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yang, Xuewei, Yu, Jiachen, Wu, Jie, Sun, Shaoning, Wang, Junjie, Yang, Yujiu
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Machine Learning
Online Access:	https://arxiv.org/abs/2606.00755
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

S2J: Bridging the Gap Between Solving and Judging Ability in Generative Reward Models
by: Sun, Shaoning, et al.
Published: (2025)

LoCa: Logit Calibration for Knowledge Distillation
by: Yang, Runming, et al.
Published: (2024)

Think-with-Rubrics: From External Evaluator to Internal Reasoning Guidance
by: Yu, Jiachen, et al.
Published: (2026)

Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models
by: Sun, Shaoning, et al.
Published: (2026)

Look Inward to Explore Outward: Learning Temperature Policy from LLM Internal States via Hierarchical RL
by: Zhou, Yixiao, et al.
Published: (2026)

Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models
by: Zhao, Siyan, et al.
Published: (2026)

Off-Policy Value-Based Reinforcement Learning for Large Language Models
by: Wang, Peng-Yuan, et al.
Published: (2026)

ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains
by: Zhao, Ziqi, et al.
Published: (2026)

Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation
by: Yuan, Qianhao, et al.
Published: (2026)

Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
by: Yang, Wenkai, et al.
Published: (2026)

ProFit: Leveraging High-Value Signals in SFT via Probability-Guided Token Selection
by: Liu, Tao, et al.
Published: (2026)

Trust Region On-Policy Distillation
by: Xing, Xingrun, et al.
Published: (2026)

LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models
by: Yang, Runming, et al.
Published: (2024)

Weight-Inherited Distillation for Task-Agnostic BERT Compression
by: Wu, Taiqiang, et al.
Published: (2023)

Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners
by: Xu, Xin, et al.
Published: (2025)

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
by: Agarwal, Rishabh, et al.
Published: (2023)

Self-Distilled Agentic Reinforcement Learning
by: Lu, Zhengxi, et al.
Published: (2026)

TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
by: Hou, Zhenyu, et al.
Published: (2025)

OISD: On-Policy Internal Self-Distillation of Language Models
by: Liu, Xinyu, et al.
Published: (2026)

Near-Policy: Accelerating On-Policy Distillation via Asynchronous Generation and Selective Packing
by: Rang, Miao, et al.
Published: (2026)

Co-Evolution of Policy and Internal Reward for Language Agents
by: Wang, Xinyu, et al.
Published: (2026)

Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
by: Shi, Chufan, et al.
Published: (2024)

DASH: Input-Aware Dynamic Layer Skipping for Efficient LLM Inference with Markov Decision Policies
by: Yang, Ning, et al.
Published: (2025)

Multi-Objective and Mixed-Reward Reinforcement Learning via Reward-Decorrelated Policy Optimization
by: Bai, Yang, et al.
Published: (2026)

Towards Token-Level Text Anomaly Detection
by: Cao, Yang, et al.
Published: (2026)

EPO: Entropy-regularized Policy Optimization for LLM Agents Reinforcement Learning
by: Xu, Wujiang, et al.
Published: (2025)

Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
by: Yang, Xinyi, et al.
Published: (2025)

BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping
by: Xi, Zhiheng, et al.
Published: (2025)

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification
by: Zhou, Yuhang, et al.
Published: (2026)

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
by: Wang, Jianze, et al.
Published: (2026)

SPEC-RL: Accelerating On-Policy Reinforcement Learning with Speculative Rollouts
by: Liu, Bingshuai, et al.
Published: (2025)

HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning
by: Wang, Weiqi, et al.
Published: (2026)

MHPO: Modulated Hazard-aware Policy Optimization for Stable Reinforcement Learning
by: Wang, Hongjun, et al.
Published: (2026)

Think When You Need: Self-Adaptive Chain-of-Thought Learning
by: Yang, Junjie, et al.
Published: (2025)

The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs
by: Li, Xin, et al.
Published: (2026)

CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimization
by: Heakl, Ahmed, et al.
Published: (2026)

Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
by: Li, Yaxuan, et al.
Published: (2026)

A Survey of On-Policy Distillation for Large Language Models
by: Song, Mingyang, et al.
Published: (2026)

Scaling Reasoning Efficiently via Relaxed On-Policy Distillation
by: Ko, Jongwoo, et al.
Published: (2026)

SODA: Semi On-Policy Black-Box Distillation for Large Language Models
by: Chen, Xiwen, et al.
Published: (2026)