BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation

2026-06-14 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

BALTO, a Balanced Token-level Policy Optimization framework, addresses large language model (LLM) hallucinations in knowledge-intensive applications. Existing reinforcement learning (RL) methods often use response-level faithfulness rewards, which suffer from a granularity mismatch, penalizing supported content due to localized errors. Even fine-grained feedback can lead to unbalanced credit assignment and biases. BALTO mitigates this by extracting factual claims, verifying them against reference contexts, and projecting judgments to token-level labels. It introduces a balanced token-level credit assignment mechanism that redistributes probability mass from unsupported content towards faithful content, rather than suppressing entire responses. Theoretically, BALTO offers advantages in training stability and optimization efficiency. Experiments on ConFiQA, RAGTruth, and FinLLM-Eval demonstrate BALTO's superior faithfulness across all six model--benchmark settings, consistently outperforming existing post-training baselines in Q-Score and showing a stronger faithfulness--informativeness trade-off.

Key takeaway

For machine learning engineers deploying large language models in knowledge-intensive applications, BALTO offers a robust solution for hallucination mitigation. If your current reinforcement learning approaches struggle with granularity mismatch or credit assignment biases, you should evaluate BALTO's balanced token-level policy optimization. It consistently achieves higher faithfulness and a stronger faithfulness--informativeness trade-off compared to existing post-training baselines, enhancing both training stability and optimization efficiency in your systems.

Key insights

BALTO mitigates LLM hallucinations by using balanced token-level policy optimization, redistributing credit to faithful content rather than suppressing entire responses.

Principles

• Response-level RL rewards cause granularity mismatch.
• Unbalanced credit assignment introduces biases.
• Focus credit on faithful content, not suppression.

Method

BALTO extracts factual claims, verifies them against reference context, projects judgments to token-level labels, and applies a balanced token-level credit assignment mechanism to redistribute probability mass.

In practice

• Mitigate LLM hallucinations in RAG systems.
• Improve faithfulness-informativeness trade-off.
• Enhance RL training stability for faithfulness.

Topics

• Large Language Models
• Hallucination Mitigation
• Reinforcement Learning
• Policy Optimization
• Token-level Credit Assignment
• Faithfulness

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.