An arXiv preprint reports that agents trained with policy-tailored versions of existing benchmark environments performed better on held-out tasks—tasks kept aside for evaluation—and used fewer interaction steps than agents trained in the original environments. In skill-based learning, the reported advantage reached 9.0 points, with 9.8% fewer steps.
How the wrapper works
EnvHarness keeps the frozen benchmark intact while adding three components: Stage reshapes starting states, Contract changes the interaction interface and Chain changes the task horizon. All three work through the standard reset/step interface, leaving the original benchmark in place.
EnvRigger treats the policy as a black box. It diagnoses vulnerabilities from successful and failed trajectories, writes candidate components, tests them with fresh rollouts and revises candidates that fail validation.
The experiments covered five benchmarks across four domains: embodied text environments, web interaction, software engineering and office automation.
The reported splits were benchmark-specific rather than one pooled sample. They included 100 ALFWorld training tasks; 20 WebArena tasks per sub-domain; 100 SWE-bench Lite training tasks and 407 held-out Verified issues; 50 OfficeQA training tasks and 172 test tasks; and 100 of 400 verified SpreadsheetBench tasks, with 299 held-out tasks containing 897 instances.
The clearest gains came in skill learning
Across skill-based learning, EnvHarness environments produced results up to 9.0 points higher than Original Envs while using 9.8% fewer interaction steps on held-out tasks.
On SWE-bench Verified, the EnvHarness skills recorded a 52.58 success rate, compared with 49.88 for Original Envs. Average execution steps were 49.61 and 55.01 respectively, and the reported changes were +2.70 points and 5.40 fewer steps.
Against specialized generation baselines, EnvHarness was 5.7 points higher on average and 8.5 points higher out of distribution on ALFWorld than GenEnv. On SWE-bench, it was 2.46 success-rate points higher than SWE-smith and used 5.11 fewer execution steps per episode.
The gains were not uniform
Reinforcement-learning results were less uniform. EnvHarness was higher on three of four reported metrics: ALFWorld in-distribution success was 87.9 versus 81.4, WebShop score was 79.2 versus 75.6, and WebShop success was 67.4 versus 66.0. On ALFWorld out of distribution, the figures were 88.8 for EnvHarness and 89.6 for Original Envs.
Performance also rose in a SWE-bench scaling analysis as the environment budget increased. EnvHarness's resolved rate moved from 47.67% to 54.79% at 300 environments, a 7.12-point gain; at the same budget, original environments reached 52.13% and generated environments 50.37%.
Across four policy models tested on SWE-bench, EnvHarness skills were 2.7 to 3.7 absolute points higher than real-environment skills. Without skills, success rates ranged from 30.7% to 67.2%.
Chain-only skills had a 49.63 success rate versus 49.88 for Original Envs and reduced average steps to 41.96. Combining Chain with Stage and Contract produced the highest reported success rate, 54.30, with 43.12 average steps.
Transfer was uneven. In a leave-one-out ALFWorld analysis, EnvHarness skills were higher on four of six task types and 3.1 points higher on average. The largest gain was 16.4 points on clean, while heat showed an 8.7-point regression.
What the evidence does not settle
The reported results are descriptive benchmark comparisons. The main ALFWorld and WebArena performance table reports means from three independent runs with standard deviations, while the reinforcement-learning comparison was nonrandomized.
The evidence is limited to benchmark agent learning: the experiments covered five benchmarks across four domains, and the framework operated through a standard reset/step interface.
The manuscript is an arXiv v1 preprint dated 20 Aug 2026, and the supplied record does not report journal publication or peer-review status.
Paper data and sources
Original title: EnvHarness: Awakening Static Worlds for Agent Learning
Authors: Chengsong Huang, Zifeng Wang, Rujun Han et al.
Journal/Repository: arXiv
Status: Preprint, not yet peer-reviewed
First online: 2026-08-20
DOI: Not available
Original paper · Full text