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Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning research paper by Snowflake, 2026

Snowflake · Feb 10, 2026 · Agents and evaluation · 40 citations · 53 upvotes · unverified

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What it shows

Large language model agents trained in synthetic environments with code-driven simulations and database-backed state transitions demonstrate superior out-of-distribution generalization compared to traditional benchmark-specific approaches.

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

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About this paper
Authors
Zhaoyang Wang, Canwen Xu, Boyi Liu and 5 more
arXiv
2602.10090 · PDF
Venue
arXiv.org
Citations
40, 9 influential · Semantic Scholar
Upvotes
53 · Hugging Face
Code
github.com/Snowflake-Labs/agent-world-model
Lab
Snowflake · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 9Sep 25, 2026
Citationsfirst count: 40Sep 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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