11 citations · 23 across the 44 of their papers we have counts for
8 papers · 1 filter
CompassOPD: Cross-Family On-Policy Distillation via Within-Family Likelihood Shifts
Naibin Gu, Qingyi Si, Chenxu Yang +5
On-policy distillation (OPD) provides dense token-level supervision on student-generated trajectories. Although OPD performs strongly when teacher and student belong to the same mo…
Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +3
Reinforcement learning with verifiable rewards (RLVR) improves the ability of large language model, yet headline accuracy gains often conceal a hidden cost: previously solved probl…
Co-Evolving Policy Distillation
Naibin Gu, Chenxu Yang, Qingyi Si +7
RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single mode…
Near-Future Policy Optimization
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning with verifiable rewards (RLVR) has become a core post-training recipe. Introducing suitable off-policy trajectories into on-policy exploration accelerates RL…
Self-Distilled RLVR
Chenxu Yang, Chuanyu Qin, Qingyi Si +7
On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals…
IRPM: Intergroup Relative Preference Modeling for Pointwise Generative Reward Models
Haonan Song, Qingchen Xie, Huan Zhu +12
Generative Reward Models (GRMs) have demonstrated strong performance in reward modeling, due to their interpretability and potential for refinement through reinforcement learning (…