arXiv (All)
open access
Priority-Preserving Advantage Aggregation for Multi-Reward Policy Optimization
LLM post-training often optimizes multiple objectives with unequal priorities, such as correctness and reasoning efficiency. However, the widely used additive reward aggregation in RL can violate these priorities: when primary and auxiliary advantages conflict, a sufficiently large auxiliary signal
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
- LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback#1327476
- The Invisible Leash: Why RLVR May or May Not Escape Its Origin#1344446
- Coopshift: Post-Training Amplifies The Memory Curse In LLM Cooperation#1388652
- PAC: Progress-Augmented Advantage Curriculum for Multi-Task Reinforcement Learning of LLMs#799720
- A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes#1327942
Record · ID 1458016
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