RLBFF: Binary Flexible Feedback to bridge between Human Feedback &
  Verifiable Rewards
Authors: name: Zhilin Wang, name: Jiaqi Zeng, name: Olivier Delalleau, name: Ellie Evans, name: Daniel Egert, name: Hoo-Chang Shin, name: Felipe Soares, name: Yi Dong, name: Oleksii Kuchaiev
Organization: NVIDIA

Reinforcement Learning with Human Feedback (RLHF) and Reinforcement Learning
with Verifiable Rewards (RLVR) are the main RL paradigms used in LLM
post-training, each offering distinct advantages. However, RLHF struggles with
interpretability and reward hacking because it relies on human judgments that
usually lack explicit criteria, whereas RLVR is limited in scope by its focus
on correctness-based verifiers. We propose Reinforcement Learning with Binary
Flexible Feedback (RLBFF), which combines the versatility of human-driven
preferences with the precision of rule-based verification, enabling reward
models to capture nuanced aspects of response quality beyond mere correctness.
RLBFF extracts principles that can be answered in a binary fashion (e.g.
accuracy of information: yes, or code readability: no) from natural language
feedback. Such principles can then be used to ground Reward Model training as
an entailment task (response satisfies or does not satisfy an arbitrary
principle). We show that Reward Models trained in this manner can outperform
Bradley-Terry models when matched for data and achieve top performance on
RM-Bench (86.2%) and JudgeBench (81.4%, #1 on leaderboard as of September 24,
2025). Additionally, users can specify principles of interest at inference time
to customize the focus of our reward models, in contrast to Bradley-Terry
models. Finally, we present a fully open source recipe (including data) to
align Qwen3-32B using RLBFF and our Reward Model, to match or exceed the
performance of o3-mini and DeepSeek R1 on general alignment benchmarks of
MT-Bench, WildBench, and Arena Hard v2 (at <5% of the inference cost).