arXiv (OAI Expanded)
open access
OISD: On-Policy Internal Self-Distillation of Language Models
Recent reinforcement learning (RL) post-training approaches primarily optimize the final output policy using sparse outcome-level rewards, while largely overlooking predictive signals encoded in intermediate representations. In this paper, we introduce a new paradigm called on-policy internal self-d
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- OISD: On-Policy Internal Self-Distillation of Language Models#793692
- SIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation#1131943
- Beyond On-Policy Exploration: Integrating External Policy Rollouts for Reinforcement Learning in Diffusion Language Models#1014783
- Towards Better Training Signal: Advantage Clipped Policy Optimization#1132017
- Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models#1076434
Record · ID 789688
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive