ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training
Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scarce, and existing synthesis methods suffer from significant limitations regarding environmental diversity and scalability. To address these challenges, we introduce ScaleEnv, a framework that constructs fully interactive environments and verifiable tasks entirely from scratch. Specifically, ScaleEnv ensures environment reliability through procedural testing, and guarantees task completeness and solvability via tool dependency graph expansion and executable action verification. By enabling agents to learn through exploration within ScaleEnv, we demonstrate significant performance improvements on unseen, multi-turn tool-use benchmarks such as τ^2-Bench and VitaBench, highlighting strong generalization capabilities. Furthermore, we investigate the relationship between increasing number of domains and model generalization performance, providing empirical evidence that scaling environmental diversity is critical for robust agent learning.
69894bebbeecc443208d25cc
Dunwei Tu
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Hongyan Hao
69894bebbeecc443208d25ce
Hansi Yang
claimed_verified
2026-02-09T08:29:52.498Z
65ced159d82f8d722c78e0cf
/avatars/a7121348e4fe77055533f14cda7f90b8.svg
Hansi Yang
animawang
user
animawang
69894bebbeecc443208d25cf
Yihao Chen
69894bebbeecc443208d25d0
Yi-Kai Zhang
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Zhikang Xia
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Yu Yang
69894bebbeecc443208d25d3
Yueqing Sun
69894bebbeecc443208d25d4
Xingchen Liu
69894bebbeecc443208d25d5
Furao Shen
69894bebbeecc443208d25d6
Qi Gu
69894bebbeecc443208d25d7
Hui Su
69894bebbeecc443208d25d8
Xunliang Cai
ScaleEnv: Scaling Environment Synthesis from Scratch for Generalist Interactive Tool-Use Agent Training
Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scarce, and existing synthesis methods suffer from significant limitations regarding environmental diversity and scalability. To address these challenges, we introduce ScaleEnv, a framework that constructs fully interactive environments and verifiable tasks entirely from scratch. Specifically, ScaleEnv ensures environment reliability through procedural testing, and guarantees task completeness and solvability via tool dependency graph expansion and executable action verification. By enabling agents to learn through exploration within ScaleEnv, we demonstrate significant performance improvements on unseen, multi-turn tool-use benchmarks such as τ^2-Bench and VitaBench, highlighting strong generalization capabilities. Furthermore, we investigate the relationship between increasing number of domains and model generalization performance, providing empirical evidence that scaling environmental diversity is critical for robust agent learning.
ScaleEnv framework generates interactive environments from scratch to improve agent generalization through diverse domain scaling and verified task completion.
generalist agents interactive environments self-exploration procedural testing tool dependency graph expansion executable action verification multi-turn tool-use benchmarks τ²-Bench VitaBench model generalization