Fast-Slow Thinking RM: Efficient Integration of Scalar and Generative Reward Models
cs.CL, cs.LG
Submitted: 2026-03-02
Updated: 2026-09-26
Terminology
Sources
- BLEUBERI: BLEU is a surprisingly effective reward for instruction following
- RM-R1: Reward Modeling as Reasoning
- Deep Think with Confidence
- Reward Reasoning Model
- Think Twice: Branch-and-Rethink Reasoning Reward Model
- Scalable Best-of-N Selection for Large Language Models via Self-Certainty
- LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
- Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs
- Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
- RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style
- Inference-Time Scaling for Generalist Reward Modeling
- Generative Reward Models
- HybridFlow: A Flexible and Efficient RLHF Framework
- JudgeBench: A Benchmark for Evaluating LLM-based Judges
- Self-rationalization improves LLM as a fine-grained judge
- PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
- J1: Incentivizing Thinking in LLM-as-a-Judge via Reinforcement Learning
- reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs
- Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models
- Generative Verifiers: Reward Modeling as Next-Token Prediction
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