arXiv (All)
open access
Can Jev be Your Q or Policy in Reinforcement Learning?
Foundation models supply reinforcement learning (RL) with priors that mitigate its longstanding weaknesses in sample efficiency and transfer, but their token-by-token generation makes queries sequential and costly. Jev, a recently released decision model, generates nothing and returns calibrated, ty
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
- From Policy Evaluation to Off-Policy Control: Provable Emergence of In-Context Q-Learning#1343251
- Nudging Beyond the Comfort Zone: Efficient Strategy-Guided Exploration for RLVR#1199839
- Large Language Models Hack Rewards, and Society#1129288
- TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs#1118301
- Unlocking the Unsolvable: Teacher-Guided Curriculum for Data-Efficient RLVR#1083480
Record · ID 1248972
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