CAMEL: Reward Models Judge Fast, Then Reflect
jiqizhixin · x · 2026-07-20
Researchers from NUS and TikTok proposed CAMEL: a confidence-gated reflection framework for reward models.
It first uses a single token for a quick preference judgment, triggering the "reflection" process only on low-confidence samples. The reflection phase combines reinforcement learning with counterfactual prefixes to improve judgment quality. The authors report an average accuracy of 82.9%, a 3.2% improvement over the previous best. Furthermore, a 14B parameter model outperforms some 70B models, achieving a new Pareto frontier in both accuracy and efficiency.
More from Research
- Structural ensembles beat single predictions in TCR:pMHC generalization study — quaidmorris · 2026-07-22
- RSS launches under OMSF to push structural biology data modeling at scale — MoAlQuraishi · 2026-07-22
- enFoldX tops 8 neoantigen scans and an unseen-peptide benchmark — quaidmorris · 2026-07-22
- enFoldX reaches AUC 0.82 on human VDJdb and transfers to mouse at 0.76 — quaidmorris · 2026-07-22
- enFoldX gains accuracy as AF3 ensemble disagreement rises for non-binders — quaidmorris · 2026-07-22
- A 3D ray plot shows how hard this Jacobian counterexample is to read — moultano · 2026-07-22