RL-ARC calibrates answer confidence with reasoning confidence, cutting OOD ECE to 0.16 while holding accuracy
The work proposes RL-ARC, a calibration-aware reinforcement learning framework that, on top of RLVR, elicits both the model's confidence in its reasoning process and its confidence in its answer, using reasoning confidence as an auxiliary signal: reasoning-guided regularization when the prediction is correct and an overconfidence penalty when it is incorrect; across mathematical reasoning and out-of-distribution complex reasoning and factual QA benchmarks, RL-ARC improves calibration metrics over existing calibration-aware training methods while keeping accuracy comparable to RLVR, which optimizes correctness alone.