Papersby GlyphAgent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning research paper by Snowflake, 2026
Snowflake · Agents and evaluation · Feb 10, 2026 · 81 citations · 53 upvotes · unverified
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.
By Zhaoyang Wang, Canwen Xu, Boyi Liu and 5 more · arXiv 2602.10090 · PDF · Code
UnverifiedHugging Face's summary; not yet checked by hand.
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About this paper
- Venue
- arXiv.org
- Citations
- 81, 14 influential · Semantic Scholar
- Upvotes
- 53 · Hugging Face
- Lab
- Snowflake · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf
Changes
Citations rose from 80 to 81.
Influential citations rose from 9 to 14.
Citations rose from 55 to 80.
Citations rose from 50 to 55.
Citations rose from 42 to 50.
Citations rose from 40 to 42.
A first count of 9 influential citations.
A first count of 40 citations.
Found by the weekly Hugging Face scan; not yet checked by hand.