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Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning research paper by Google, 2025

Google · Oct 29, 2025 · Reasoning · 11 citations · 48 upvotes · unverified

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What it shows

Supervised Reinforcement Learning (SRL) enhances small-scale LLMs' multi-step reasoning by generating internal monologues and using step-wise expert actions for richer learning signals, outperforming SFT and RLVR.

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

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ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.NVIDIA · Jul 2025 · Unverified172
WebShaper: Agentically Data Synthesizing via Information-Seeking FormalizationWebShaper, a formalization-driven framework, synthesizes information-seeking datasets using set theory and Knowledge Projections to enhance reasoning structure and achieve top performance in open-sourced benchmarks.Alibaba (Qwen) · Jul 2025 · Unverified113
About this paper
Authors
Yihe Deng, I-Hung Hsu, Jun Yan and 7 more
arXiv
2510.25992 · PDF
Venue
arXiv.org
Citations
11, 1 influential · Semantic Scholar
Upvotes
48 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 1Sep 25, 2026
Citationsfirst count: 11Sep 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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