OPERA: Aligning Open-Ended Reasoning via Objective Perplexity-based Reinforcement Learning
Reinforcement Learning (RL) has enabled LLMs to excel in objective reasoning tasks such as mathematics and code generation. However, applying RL to open-ended tasks, such as creative writing, remains challenging because LLM-as-a-judge reward models often exhibit stylistic biases and positional inconsistencies, leading to unstable supervision. To address this, we propose OPERA (Objective Perplexity-based Reflective Alignment), which replaces unreliable external judges with intrinsic rewards derived from perplexity dynamics. Specifically, we derive an intrinsic reward signal from perplexity dynamics, quantifying uncertainty reduction at critical reflective states. During the cold-start phas...
| Authors | Wenxuan Jiang, Zining Fan, Zijian Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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