arXiv (All)
open access
UniOPSD: Unifying Outcome and Hindsight Feedback for Agentic Reinforcement Learning
Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment across long interaction sequences. Recent work on on-policy self-distillation (OPSD) offers complementary supervision by ev
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
- Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation#1160742
- Beyond Token-Local Imitation: Reward-Compatible Temporal Credit Assignment for On-Policy Distillation#919416
- TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models#1118126
- Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models#1076434
- Reward as Observation: Learning Reward-Based Policies for Rapid Adaptation#1155664
Record · ID 1129427
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