OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning
- URL: http://arxiv.org/abs/2605.12400v1
- Date: Tue, 12 May 2026 17:00:53 GMT
- Title: OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning
- Abstract summary: We study on-policy self-distillation (OPSD), where a language model improves its reasoning ability by distilling privileged teacher distributions along its own on-policy trajectories.<n>Despite the performance gains of OPSD, we identify a common but often overlooked mismatch between teacher and student responses.
- Score: 19.98950359294245
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study {on-policy self-distillation} (OPSD), where a language model improves its reasoning ability by distilling privileged teacher distributions along its own on-policy trajectories. Despite the performance gains of OPSD, we identify a common but often overlooked mismatch between teacher and student responses: self-reflected teacher responses can be shifted by reflection-induced bias and response templates, leading to miscalibrated token-level supervision. To mitigate this issue, we propose \methodname, an outcome-guided logit-steering framework that leverages verifiable outcome rewards to contrast successful and failed on-policy trajectories and calibrate teacher logits. By combining outcome-level correctness with dense token-level guidance through logit steering, \methodname stabilizes self-distillation and improves reasoning performance over standard OPSD and other variants across diverse benchmarks.
Related papers
- Adaptive Supervised Anchoring for On-Policy Self-Distillation [15.445625053566244]
On-policy self-distillation adapts a language model by distilling guidance from a frozen teacher on trajectories sampled from the student.<n>We show that when student rollouts drift from target trajectories, conditioning the teacher on off-target prefixes substantially weakens its task-relevant supervision.<n>We propose a unified training framework that separates two complementary supervision pathways.
arXiv Detail & Related papers (2026-08-08T05:46:32Z) - Group-Reflective Self-Distillation for Agentic Reinforcement Learning [8.041052886037766]
Group-Reflective Self-Distillation (GRSD) derives capability-aligned and outcome-discriminative guidance from the policy's own verified rollouts.<n>GRSD consistently outperforms competitive baselines and generalizes more effectively to unseen tasks.
arXiv Detail & Related papers (2026-07-30T11:49:46Z) - H$^2$SD: Hybrid Hindsight Self-Distillation [61.71131241473028]
Reinforcement learning with verifiable rewards (RLVR) provides reliable outcome supervision for language model reasoning.<n>Existing self-distillation methods add a privileged teacher but typically assign it a fixed role.<n>We introduce Hybrid Hindsight Self-Distillation ($mathrmH2mathrmSD$), which jointly adapts teacher context and update strategy to trajectory correctness.
arXiv Detail & Related papers (2026-07-21T10:47:27Z) - dOPSD: On-Policy Self-Distillation for Diffusion Language Models [52.60302464420127]
Diffusion large language models generate text by iteratively denoising a masked sequence.<n>Supervised fine-tuning is off-policy and suffers from exposure bias, while reinforcement learning gives only sparse, sequence-level rewards.<n>On-policy self-distillation (OPSD) offers a promising alternative, using one model as both student and teacher.
arXiv Detail & Related papers (2026-07-05T17:47:29Z) - Purified OPSD: On-Policy Self-Distillation Without Losing How to Think [52.879387744920415]
We find that on-policy self-distillation (OPSD) consistently fails on long chain-of-thought (long-CoT) reasoning models.<n>We propose a two-step solution: first, we construct a reference-only teacher to isolate the non-transferable component of the supervision signal.<n>Second, we use pointwise mutual information (PMI) as the mechanism to transform this residual into a well-formed PMI target distribution.
arXiv Detail & Related papers (2026-07-02T14:33:07Z) - Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation [60.55792673956761]
We propose textbfRubric-Conditioned Self-Distillation, a framework that incorporates rubrics as structured, fine-grained feedback for on-policy self-distillation.<n>We evaluate on a diverse suite of science reasoning benchmarks and results show that rubric-conditioned self-distillation effectively converts rubric-level criteria into token-level guidance.
arXiv Detail & Related papers (2026-06-17T17:54:04Z) - Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning [12.363923974156636]
Reinforcement learning from verifiable rewards assigns a single scalar to each rollout.<n>On-policy self-distillation addresses this by letting the same model act as a teacher conditioned on privileged information.<n>We propose Hindsight Self-Distillation (HSD), which conditions the teacher on a successful peer rollout.
arXiv Detail & Related papers (2026-06-14T03:37:27Z) - Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning [41.384652481442735]
We propose textbfDirection-Adaptive Self-Distillation (textbfDASD), which reframes privileged self-distillation from uniform teacher imitation into entropy-routed directional supervision.<n>Across six mathematical reasoning benchmarks, DASD achieves the best macro Avg@16 over strong RLVR and self-distillation baselines.
arXiv Detail & Related papers (2026-05-21T10:07:46Z) - Prefix Teach, Suffix Fade: Local Teachability Collapse in Strong-to-Weak On-Policy Distillation [49.117085054884676]
On-policy distillation trains a student model on its own rollouts using dense feedback from a stronger teacher.<n>We operationalize this principle through a trajectory-specific release rule.<n> Experimental results across strong-to-weak distillation tasks indicate that this release rule consistently outperforms standard full-trajectory OPD.
arXiv Detail & Related papers (2026-05-13T15:05:30Z) - Teacher-Guided Policy Optimization for LLM Distillation [90.49982387646861]
Teacher-Guided Policy Optimization (TGPO) is an on-policy algorithm that incorporates dense directional guidance by leveraging teacher predictions conditioned on the student's rollout.<n>Experiments on complex reasoning benchmarks demonstrate that TGPO significantly outperforms standard baselines and is robust to different teachers.
arXiv Detail & Related papers (2026-05-13T09:20:03Z) - On-Policy Distillation with Best-of-N Teacher Rollout Selection [54.91780727674628]
We propose BRTS, a Best-of-N Rollout Teacher Selection framework for on-policy distillation.<n>BRTS augments standard student-context OPD with a teacher-context supervision branch constructed from the teacher trajectory.<n>BRTS improves over standard OPD on challenging reasoning benchmarks, with the largest gains on harder datasets.
arXiv Detail & Related papers (2026-05-10T19:49:00Z) - Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization [18.027254451537342]
Existing self-distillation methods largely reduce learning to KL matching toward the context-augmented teacher model.<n>We propose textbfPreference-textbfBased textbfSelf-textbfDistillation (textbfPBSD), which revisits on-policy self-distillation through a reward-regularized perspective.
arXiv Detail & Related papers (2026-05-06T15:31:50Z) - The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation [67.26315138466312]
We propose a calibration-aware OPD framework, CaOPD, that estimates empirical confidence from model rollouts.<n>Our findings highlight that capability distillation does not imply confidence, and that confidence should be treated as an essential objective in post-training.
arXiv Detail & Related papers (2026-04-18T04:43:40Z) - SCOPE: Signal-Calibrated On-Policy Distillation Enhancement with Dual-Path Adaptive Weighting [17.504616835765617]
We propose a dual-path adaptive training framework that routes on-policy rollouts by correctness into two complementary supervision paths.<n>SCOPE achieves an average relative improvement of 11.42% in Avg@32 and 7.30% in Pass@32 over competitive baselines.
arXiv Detail & Related papers (2026-04-12T15:26:14Z) - Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval [26.186599286240362]
We propose a Stratified Sampling strategy that uniformly covers the entire score spectrum.<n>Experiments confirm that Stratified Sampling, which preserves the variance and entropy of teacher scores, serves as a robust baseline.
arXiv Detail & Related papers (2026-04-06T15:02:35Z) - KEPO: Knowledge-Enhanced Preference Optimization for Reinforcement Learning with Reasoning [24.072603982041798]
Reinforcement learning has emerged as a promising paradigm for inducing explicit reasoning behaviors in large language and vision-language models.<n>However, reasoning-oriented RL post-training remains fundamentally challenging due to sparse trajectory-level rewards.<n>Recent on-policy distillation methods introduce dense teacher supervision to stabilize optimization, but apply it uniformly across all generated trajectories.
arXiv Detail & Related papers (2026-01-30T23:28:37Z) - Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models [44.041109669153506]
On-Policy Self-Distillation (OPSD) is a framework where a single model acts as both teacher and student by conditioning on different contexts.<n>We demonstrate the efficacy of our method on multiple mathematical reasoning benchmarks.
arXiv Detail & Related papers (2026-01-26T17:56:50Z) - AdaSwitch: Adaptive Switching Generation for Knowledge Distillation [58.647880811071495]
Small language models (SLMs) are crucial for applications with strict latency and computational constraints.<n>We propose AdaSwitch, a novel approach that combines on-policy and off-policy generation at the token level.<n>AdaSwitch consistently improves accuracy, offering a practical and effective method for distilling SLMs with acceptable additional overhead.
arXiv Detail & Related papers (2025-10-09T06:38:37Z) - Can Large Reasoning Models Self-Train? [51.0277533541394]
We use majority voting as a simple self-feedback mechanism to study whether self-training can be sustained within reinforcement learning.<n>We find that this basic approach improves not only the model's reasoning performance, but also its capability of generating better quality feedback for the next RL iteration.<n>Yet our analysis also reveals a critical limitation of such a self-training paradigm - prolonged RL with self-reward leads to reward hacking, resulting in sudden and complete performance collapse.
arXiv Detail & Related papers (2025-05-27T17:16:00Z) - Learning Domain Adaptive Object Detection with Probabilistic Teacher [93.76128726257946]
We present a simple yet effective framework, termed as Probabilistic Teacher (PT)
PT aims to capture the uncertainty of unlabeled target data from a gradually evolving teacher and guides the learning of a student in a mutually beneficial manner.
We also present a novel Entropy Focal Loss (EFL) to further facilitate the uncertainty-guided self-training.
arXiv Detail & Related papers (2022-06-13T16:24:22Z) - Improving Self-supervised Learning with Automated Unsupervised Outlier
Arbitration [83.29856873525674]
We introduce a lightweight latent variable model UOTA, targeting the view sampling issue for self-supervised learning.
Our method directly generalizes to many mainstream self-supervised learning approaches.
arXiv Detail & Related papers (2021-12-15T14:05:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.