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Reducing Credit Assignment Variance via Counterfactual Reasoning Paths

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arXiv:2605. 16302v1 Announce Type: new Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on sparse terminal rewards, leading to poor credit assignment conditions where the final feedback is evenly propagated across all intermediate decisions.

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Authors: Fei Ding, Yongkang Zhang, Yeling Peng, Youwei Wang, Guoxiong Zhou, Zijian Zeng
Released: 2026-05-19 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Reducing Credit Assignment Variance via Counterfactual Reasoning Paths overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, rect, under, propagate, language, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.16302

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