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arXiv AI By Qiming Xie, Wenjie Zheng, Xiangqing Shen, Rui Xia

FARCA: Fact-Aligned Reliability-Aware Credit Assignment for Reinforcement Learning with Factual Supervision

Read the original on arXiv AI →

FARCA is a policy‑optimization framework that addresses the problem of noisy factual credit assignment in reinforcement learning for large language models. It transforms coarse factual supervision into token‑level training signals that are both localized and weighted by a reliability estimate derived from counterfactual evidence attribution. Experiments on several factual reasoning benchmarks show that FARCA improves model factuality while maintaining general reasoning abilities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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