Cover Image

PACED: Distillation and On-Policy Self-Distillation at the Frontier of Student Competence

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Yuanda, Sang, Hejian, Zhou, Zhengze, He, Ran, Wang, Zhipeng
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TIP: Token Importance in On-Policy Distillation
by: Xu, Yuanda, et al.
Published: (2026)

Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
by: Xu, Yuanda, et al.
Published: (2026)

Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning
by: Xu, Yuanda, et al.
Published: (2026)

Not all tokens are needed(NAT): token efficient reinforcement learning
by: Sang, Hejian, et al.
Published: (2026)

CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
by: Sang, Hejian, et al.
Published: (2026)

Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints
by: Markovic-Voronov, Jelena, et al.
Published: (2026)

Debunk the Myth of SFT Generalization
by: Lin, Xiaofeng, et al.
Published: (2025)

Aligning Diffusion Language Models via Unpaired Preference Optimization
by: Jindal, Vaibhav, et al.
Published: (2025)

HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation
by: Ding, Ken
Published: (2026)

OISD: On-Policy Internal Self-Distillation of Language Models
by: Liu, Xinyu, et al.
Published: (2026)

Extreme Region Policy Distillation
by: Chen, Changyu, et al.
Published: (2026)

Proximal Policy Distillation
by: Spigler, Giacomo
Published: (2024)

Online Policy Distillation with Decision-Attention
by: Yu, Xinqiang, et al.
Published: (2024)

daDPO: Distribution-Aware DPO for Distilling Conversational Abilities
by: Zhang, Zhengze, et al.
Published: (2025)

When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
by: Liu, Xiaogeng, et al.
Published: (2026)

TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment
by: Wang, Jiaxuan, et al.
Published: (2026)

Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing
by: Li, Gengsheng, et al.
Published: (2026)

From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation
by: Shen, Guobin, et al.
Published: (2026)

OGLS-SD: On-Policy Self-Distillation with Outcome-Guided Logit Steering for LLM Reasoning
by: Yang, Yuxiao, et al.
Published: (2026)

On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
by: Agarwal, Rishabh, et al.
Published: (2023)

Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning
by: Yang, Zhicheng, et al.
Published: (2026)

AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation
by: Zhang, Songming, et al.
Published: (2025)

Variational Distillation of Diffusion Policies into Mixture of Experts
by: Zhou, Hongyi, et al.
Published: (2024)

FedSDR: Federated Self-Distillation with Rectification
by: Ren, Ziheng, et al.
Published: (2026)

IPD: Boosting Sequential Policy with Imaginary Planning Distillation in Offline Reinforcement Learning
by: Qin, Yihao, et al.
Published: (2026)

OPD+: Rethinking the Advantage Design for On-Policy Distillation
by: Zhao, Hanyang, et al.
Published: (2026)

Trust-Region Behavior Blending for On-Policy Distillation
by: Plyusov, Daniil, et al.
Published: (2026)

Reinforcement Learning via Self-Distillation
by: Hübotter, Jonas, et al.
Published: (2026)

Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation
by: Chen, Wei-Rui, et al.
Published: (2025)

Annealing Self-Distillation Rectification Improves Adversarial Training
by: Wu, Yu-Yu, et al.
Published: (2023)

RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
by: Xu, Charles, et al.
Published: (2024)

Distilled Protein Backbone Generation
by: Xie, Liyang, et al.
Published: (2025)

The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation
by: Zhang, Jiaxin, et al.
Published: (2026)

ADWIN: Adaptive Windows for Horizon-Aware On-Policy Distillation
by: Liang, Kun, et al.
Published: (2026)

Stable On-Policy Distillation through Adaptive Target Reformulation
by: Jang, Ijun, et al.
Published: (2026)

Interpretable Policy Distillation for Power Grid Topology Control
by: Dmitruka, Aleksandra, et al.
Published: (2026)

Evaluating Interpretable Reinforcement Learning by Distilling Policies into Programs
by: Kohler, Hector, et al.
Published: (2025)

Self-Distilled Disentangled Learning for Counterfactual Prediction
by: Li, Xinshu, et al.
Published: (2024)

GNN's Uncertainty Quantification using Self-Distillation
by: Daneshvar, Hirad, et al.
Published: (2025)

SODA: Semi On-Policy Black-Box Distillation for Large Language Models
by: Chen, Xiwen, et al.
Published: (2026)