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FIPO: Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization research paper by Alibaba (Qwen), 2026

Alibaba (Qwen) · Mar 20, 2026 · Reasoning · 21 citations · 104 upvotes · unverified

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What it shows

FIPO enhances reinforcement learning for language models by using discounted future-KL divergence to improve credit assignment and extend reasoning chains, achieving better mathematical problem-solving performance.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Chiyu Ma, Shuo Yang, Kexin Huang and 7 more
arXiv
2603.19835 · PDF
Venue
arXiv.org
Citations
21, 3 influential · Semantic Scholar
Upvotes
104 · Hugging Face
Code
github.com/qwenpilot/FIPO
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 3Sep 25, 2026
Citationsfirst count: 21Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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