The paper introduces Un-OPD, an unbiased on‑policy distillation framework for block diffusion language models that addresses two key optimization biases: context misalignment from mismatched block boundaries and an intrinsic bias that causes overconfidence collapse. Un-OPD employs a boundary‑aware step filtering strategy to remove misaligned decoding steps and a support‑rebalanced confidence calibration to moderate optimization intensity at high‑support positions. The method also includes a rollout reuse mechanism to cut generation overhead, and experiments on math reasoning and code generation tasks demonstrate stable training, improved performance, and roughly 50% faster wall‑clock time.
arXiv:2608. 03632v1 Announce Type: new Abstract: On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals.
The paper investigates the learning dynamics of on‑policy distillation (OPD), a method that combines teacher supervision with training on student‑generated trajectories. It identifies two stages of the feedback loop: converting the distillation signal into parameter updates, and broadcasting those updates to alter predictions and reshape the rollout distribution. The study explains why students may be unresponsive to teacher disagreement and how updates that improve local agreement can lead to degeneration by increasing the likelihood of poor continuations. It also shows that offline distillation can prepare a student for OPD without boosting immediate accuracy, yet OPD can still yield significant accuracy gains after sufficient offline preparation.
arXiv:2605. 03677v2 Announce Type: replace Abstract: On-policy distillation (OPD) has recently emerged as an effective post-training paradigm for consolidating the capabilities of specialized expert models into a single student model.
The paper proposes Mixture Proposal On-Policy Distillation (MPOPD) to address a mismatch issue in on‑policy distillation (OPD) where the rollout policy is fixed to the current student. By treating the rollout policy as an optimization target, MPOPD derives a geometric mixture of student and teacher policies and introduces a proposal policy for efficient rollout generation. Experiments on 16 reasoning benchmarks show consistent gains over OPD, notably a 6.91% accuracy improvement on math reasoning with Qwen3-4B-Base and a 41% reduction in generation length.
On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable.
SOD: Step-wise On-policy Distillation for Small Language Model Agents proposes a new framework that adaptively reweights distillation strength at each reasoning step based on step-level divergence. This approach mitigates cascading errors in tool-integrated reasoning by attenuating misleading teacher signals in high-divergence regions while preserving dense guidance where student and teacher align. Experiments on math, science, and code benchmarks show up to 20.86% improvement over the second-best baseline, with a 0.6B student scoring 26.13% on AIME 2025.
arXiv:2606. 08432v1 Announce Type: new Abstract: On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts.
The paper introduces GFD-OPD, a method for on‑policy distillation of diffusion models that addresses challenges when compressing large teachers into smaller students. It identifies that standard distillation fails due to distribution gaps and classifier‑free guidance amplification, and proposes Fixed‑State KL to measure these gaps. GFD‑OPD reduces the student‑teacher discrepancy and achieves state‑of‑the‑art performance across multiple benchmarks.
arXiv:2605.17862v2 Announce Type: replace-cross Abstract: Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary fo...
arXiv:2608. 09447v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch of offline distillation.
arXiv:2609.08341v1 Announce Type: new Abstract: On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (L...
arXiv:2605.07865v2 Announce Type: replace-cross Abstract: On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for Large Language Models, especially in reasoning domains. How...