arXiv (All)
open access
Lexicographic Multi-Objective On-Policy Distillation
Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-quality reasoning and concise responses. Existing multi-reward post-training methods typically scalarize rewards or combine specialists without explicitly
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
- Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards#1355803
- NLPG: Natural-Language Policy Gradients for Self-Evolving Language Agents#1118091
- RedFlow: Redirect Failure into Action-level Corrections for Flow-matching VLA Policy#1118671
- Survey on reinforcement learning for language processing#176446
- Learning Ordinal Probabilistic Reward from Preferences#1326204
Record · ID 1186673
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