BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation
Hallucinations remain a major obstacle to deploying large language models (LLMs) in knowledge-intensive settings, where generated responses must be faithfully grounded in provided evidence. Reinforcement learning (RL) is a promising direction for hallucination mitigation, but response-level faithfulness rewards suffer from a granularity mismatch: localized hallucinations can cause supported content to receive spurious penalties. Although recent work introduces fine-grained feedback such as claim-level verification and token-level rewards, unbalanced credit assignment can still induce length, verbosity, or optimization-noise biases. We propose BALTO, a Balanced Token-level Policy Optimizat...
| Authors | Ning Li, Zixuan Guo, Yan Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 June 2026 |
| Last updated at source | 22 June 2026 |
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