EGRSD and CL-EGRSD advance the accuracy-length frontier in LLM reasoning by entropy-guided weighting of token-level distillation signals from the teacher.
GATES: Self-distillation under privileged context with consensus gating
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Disagreement-modulated reverse-KL barycenter targets let on-policy self-distillation attenuate privileged leakage while preserving exploration, beating SDPO and GRPO on SciKnowEval and GPQA.
ReNIO reweights negative student-generated trajectories in LLM on-policy distillation using probability ratios, reporting relative gains up to 10% on reasoning benchmarks.
SGCD reshapes GRPO token advantages via detached sibling-contrast credit from an external LLM, improving AppWorld and airline tool-use scores while keeping policy gradient as the actor update.
Visual-SDPO distills visual feedback from rendered code outputs into a student policy via grounded credit weighting and GRPO, yielding over 10-point gains on chart/UI/slide benchmarks.
Sleep-time Knowledge Seeding plus Dreaming lets LLMs expand capacity, distill fragile in-context memories into stable parameters, and self-improve without human labels.
DASD improves math reasoning in LLMs by adaptively directing self-distillation based on per-token entropy to balance exploration and step accuracy, outperforming prior self-distillation and RLVR baselines on six benchmarks.
Local teachability collapse occurs in later trajectory segments during strong-to-weak OPD; a margin-based release rule using top-K teacher advantage and BIC change-point detection on sentence segments outperforms full-trajectory supervision on five in-domain benchmarks and preserves out-of-domain pe
ATESD introduces a Beta-policy controller that adapts teacher exposure ratio during LLM self-distillation training and reports gains over fixed-exposure baselines on math benchmarks.
Uni-OPD improves on-policy distillation via student-side data balancing for informative rollouts and teacher-side outcome-guided margin calibration that restores order consistency with rewards.
DOPD is an advantage-aware dual distillation method that dynamically assigns token supervision from either privileged teacher or student to transfer capability while mitigating non-replicable information asymmetry in on-policy distillation.
Reshaping outcome rewards, process signals, and rollout comparability in GRPO raises strict compile-and-semantic accuracy in agentic code repair from 0.385 to 0.535 under weak feedback.
This overview paper explains the conceptual foundations and design principles of On-Policy Self-Distillation for large language models from a beginner's perspective.
citing papers explorer
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Respecting Self-Uncertainty in On-Policy Self-Distillation for Efficient LLM Reasoning
EGRSD and CL-EGRSD advance the accuracy-length frontier in LLM reasoning by entropy-guided weighting of token-level distillation signals from the teacher.
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DemoPSD: Disagreement-Modulated Policy Self-Distillation
Disagreement-modulated reverse-KL barycenter targets let on-policy self-distillation attenuate privileged leakage while preserving exploration, beating SDPO and GRPO on SciKnowEval and GPQA.
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ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation
ReNIO reweights negative student-generated trajectories in LLM on-policy distillation using probability ratios, reporting relative gains up to 10% on reasoning benchmarks.
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Keep Policy Gradient in Charge: Sibling-Guided Credit Distillation for Long-Horizon Tool-Use Agents
SGCD reshapes GRPO token advantages via detached sibling-contrast credit from an external LLM, improving AppWorld and airline tool-use scores while keeping policy gradient as the actor update.
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Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts
Visual-SDPO distills visual feedback from rendered code outputs into a student policy via grounded credit weighting and GRPO, yielding over 10-point gains on chart/UI/slide benchmarks.
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Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
Sleep-time Knowledge Seeding plus Dreaming lets LLMs expand capacity, distill fragile in-context memories into stable parameters, and self-improve without human labels.
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Tailoring Teaching to Aptitude: Direction-Adaptive Self-Distillation for LLM Reasoning
DASD improves math reasoning in LLMs by adaptively directing self-distillation based on per-token entropy to balance exploration and step accuracy, outperforming prior self-distillation and RLVR baselines on six benchmarks.
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Prefix Teach, Suffix Fade: Local Teachability Collapse in Strong-to-Weak On-Policy Distillation
Local teachability collapse occurs in later trajectory segments during strong-to-weak OPD; a margin-based release rule using top-K teacher advantage and BIC change-point detection on sentence segments outperforms full-trajectory supervision on five in-domain benchmarks and preserves out-of-domain pe
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Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning
ATESD introduces a Beta-policy controller that adapts teacher exposure ratio during LLM self-distillation training and reports gains over fixed-exposure baselines on math benchmarks.
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Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe
Uni-OPD improves on-policy distillation via student-side data balancing for informative rollouts and teacher-side outcome-guided margin calibration that restores order consistency with rewards.
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DOPD: Dual On-policy Distillation
DOPD is an advantage-aware dual distillation method that dynamically assigns token supervision from either privileged teacher or student to transfer capability while mitigating non-replicable information asymmetry in on-policy distillation.
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Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair
Reshaping outcome rewards, process signals, and rollout comparability in GRPO raises strict compile-and-semantic accuracy in agentic code repair from 0.385 to 0.535 under weak feedback.
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A Brief Overview: On-Policy Self-Distillation In Large Language Models
This overview paper explains the conceptual foundations and design principles of On-Policy Self-Distillation for large language models from a beginner's perspective.