1
Trust Region Policy Distillation
将信任区域优化引入策略蒸馏，解决模型压缩中的策略漂移问题。
arXiv:2607.04751v1 Announce Type: cross Abstract: Big goals are hard to achieve all at once; breaking them into small steps is wiser. We present Trust…
2
AsyncOPD: How Stale Can On-Policy Distillation Be?
On-policy蒸馏中旧数据的容忍度如何？这篇论文挑战了传统认知，给出定量分析框架。
arXiv:2606.24143v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts guided by teacher feedback and is be…
3
ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation
针对LLM在线蒸馏中负轨迹重要性不均衡问题，提出重加权方法提升推理效果
arXiv:2606.23104v1 Announce Type: cross Abstract: On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated…
4
Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
突破不同模型家族间的分词器壁垒，提出在策略蒸馏新方法，提升跨模型知识迁移效果。
arXiv:2606.09456v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models…
5
Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization
用全局归一化稳定多模态大模型基于策略的蒸馏，提升推理性能与训练效率的创新方法。
arXiv:2606.09091v1 Announce Type: new Abstract: On-policy distillation (OPD) has recently emerged as an important post-training paradigm. By using a s…
6
SafeSteer: Localized On-Policy Distillation for Efficient Safety Alignment
大模型安全新方案：局部在策略蒸馏实现高效安全对齐，论文已提交EMNLP 2026。
arXiv:2606.02530v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human values often degrades their general capabilities, ter…
7
Trust-Region Behavior Blending for On-Policy Distillation
在线策略蒸馏新突破，信任区域行为混合让策略学习更稳定高效。
arXiv:2605.31159v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching…
8
Counteraction-Aware Multi-Teacher On-Policy Distillation for General Capability Recovery with Domain Preservation
多教师蒸馏新方法，通过反作用感知策略蒸馏实现通用能力恢复并保持领域特性，为模型微调与领域延续提供新思路。
arXiv:2605.27115v1 Announce Type: new Abstract: Domain specialization can improve LLM behavior in vertical domains, but often weakens the general capa…
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A Survey of On-Policy Distillation for Large Language Models
首份大模型在线策略蒸馏综述，系统梳理方法、挑战与未来方向，适合研究者深挖。
arXiv:2604.00626v3 Announce Type: replace Abstract: As Large Language Models (LLMs) continue to grow in both capability and cost, transferring frontie…
10
Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
介绍一种批评驱动Voronoi量化方法，实现深度强化学习策略向可解释模型的高效蒸馏，解决性能-可解释性权衡难题。
arXiv:2605.14897v1 Announce Type: cross Abstract: Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillati…