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Soft Adaptive Policy Optimization research paper by Alibaba (Qwen), 2025

Alibaba (Qwen) · Nov 25, 2025 · Reasoning · 79 citations · 44 upvotes · unverified

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What it shows

Soft Adaptive Policy Optimization (SAPO) enhances the stability and performance of reinforcement learning in large language models by adaptively attenuating off-policy updates with a smooth, temperature-controlled gate, leading to improved training stability and performance.

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

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About this paper
Authors
Chang Gao, Chujie Zheng, Xiong-Hui Chen and 7 more
arXiv
2511.20347 · PDF
Venue
arXiv.org
Citations
79, 23 influential · Semantic Scholar
Upvotes
44 · Hugging Face
Code
github.com/modelscope/ms-swift
Lab
Alibaba (Qwen) · on Companies · on Quarterly

Changes

What changed
Influential citationsfirst count: 23Sep 25, 2026
Citationsfirst count: 79Sep 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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