DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment
cs.LG, cs.AI
Submitted: 2026-09-23
Updated: 2026-09-23
Terminology
Sources
- Constitutional AI: Harmlessness from AI Feedback
- MathArena: Evaluating LLMs on Uncontaminated Math Competitions
- ODIN: Disentangled Reward Mitigates Hacking in RLHF
- Training Verifiers to Solve Math Word Problems
- RLHF Workflow: From Reward Modeling to Online RLHF
- RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models
- RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
- Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
- A Survey of Reinforcement Learning from Human Feedback
- Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
- Inference-Time Scaling for Generalist Reward Modeling
- Topology and geometry of data manifold in deep learning
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Language Models Learn to Mislead Humans via RLHF
- Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models
- Reward Model Routing in Alignment
- Ask a Strong LLM Judge when Your Reward Model is Uncertain
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