aiqbench analysis
The paper proposes building LLM agent training environments from reusable parts (intent, rule, data, and server components) and revising those parts when an environment gives unreliable feedback, instead of generating each environment from scratch. The reported gains are mainly from supervised fine-tuning (16.0% to 32.0% on AutomationBench), while the RL improvement is modest (37.6% to 40.3%). These are preprint, self-reported results, so readers should watch for independent replication and for whether the reuse approach holds up on applications beyond those in the corpus.
LegoEnv: Self-Evolving Agentic Environments via Modular Recombination
arXiv:2611.01834v1 Announce Type: cross Abstract: LLM agents learn by interacting with environments, yet existing environments are designed for specific tasks and fail to generalize to new ones. Recent methods automate environment construction with LLMs. However, they generate each environment from scratch, neither reusing parts shared across environments nor carrying a fix from one environment to the rest. To address this, we propose a new paradigm of evolving environment atoms, which composes environments from reusable atoms (intent, rule, data and server) and evolves the atoms when an environment fails to give reliable feedback (e.g., an application lacks a tool the task requires). To instantiate this paradigm, we introduce LegoEnv, which builds 5,465 verified environments over 223 applications, and one round of evolution raises the share of solvable environments from 71.6% to 88.3%. On AutomationBench, SFT on these environments lifts Qwen3.6-27B from 16.0% to 32.0%, and RL with their verifiers lifts Qwen3.8-27B from 37.6% to 40.3%. Further analyses show steady gains with corpus size, transfer to unseen applications, and failures concentrated in a few shared root causes.
Abstract:LLM agents learn by interacting with environments, yet existing environments are designed for specific tasks and fail to generalize to new ones. Recent methods automate environment construction with LLMs. However, they generate each environment from scratch, neither reusing parts shared across environments nor carrying a fix from one environment to the rest. To address this, we propose a new paradigm of evolving environment atoms, which composes environments from reusable atoms (intent, rule, data and server) and evolves the atoms when an environment fails to give reliable feedback (e.g., an application lacks a tool the task requires). To instantiate this paradigm, we introduce LegoEnv, which builds 5,465 verified environments over 223 applications, and one round of evolution raises the share of solvable environments from 71.6% to 88.3%. On AutomationBench, SFT on these environments lifts Qwen3.6-27B from 16.0% to 32.0%, and RL with their verifiers lifts Qwen3.8-27B from 37.6% to 40.3%. Further analyses show steady gains with corpus size, transfer to unseen applications, and failures concentrated in a few shared root causes.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2611.01834 [cs.CL]
(or arXiv:2611.01834v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.01834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 268 words