ConsensusBench: Benchmark of Consensus Nodes for LLM Reasoning via Outcome Reward Densifying
Reinforcement learning (RL) has become one of the primary paradigms for reasoning enhancement of large language models (LLMs). In particular, Group Relative Policy Optimization (GRPO) and related algorithms have demonstrated strong performance with outcome-level rewards. However, these methods depend solely on the final answer, without feedback regarding which intermediate steps contribute to success or failure. As task complexity and reasoning trajectory length increase, such sparse final-answer rewards become increasingly insufficient. To address this limitation, we introduce ConsensusBench, a novel dataset designed to provide rule-based process-level signals. We posit that a correct fi...
| Authors | Shi-Qi Yan, Chao-Hong Tan, Qian Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 September 2026 |
| Last updated at source | 4 September 2026 |
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