We have 5 of 8 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger teacher on its own rollouts. Recent studies have improved OPD through alternative distillation reward formulations and teacher configurations, while the objective of di...
It is found that full trajectories provide only limited benefit, while partial trajectories are effective even under heavy truncation, and training LLMs using endpoints leads to consistent changes in reasoning behavior, and that it also benefits post-training methods based on reinforcement learning or on-policy distill...
This work finds that reasoning operations are separable in held-out representations, with separability peaking in middle layers, and verify that this structure is not explained by lexical or positional confounds.
It is shown that the delta signal substantially improves on-policy distillation and the new distillation method is referred to as On-Policy Delta Distillation (OPD), enabling reasoning LLMs to achieve strong performance with only a short post-training period.
It is found that English-only OPD can also increase performance for Korean and Japanese, but often shifts the responses toward English, highlighting the importance of multilingual data to preserving target-language responses.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
