ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation
arXiv:2610.02678v1 Announce Type: new Abstract: On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher, but providing
arXiv:2605.11458v1 Announce Type: new Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own
arXiv:2605.11182v1 Announce Type: new Abstract: On-policy distillation (OPD) and on-policy self-distillation (OPSD) have emerged as promising post-training methods for large
arXiv:2607.05804v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a
arXiv:2604.09852v1 Announce Type: new Abstract: Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state.
arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder
arXiv:2608.27857v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer a path to energy-efficient language modeling through sparse encoding and event-driven
arXiv:2607.21793v1 Announce Type: new Abstract: Recent large reasoning models often develop long chain-of-thought responses during reinforcement learning (RL), resulting in high
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