EnvHarness: Awakening Static Worlds for Agent Learning
A wrapper called EnvHarness adapts static agent environments without changing their underlying verifiers.
The paper says EnvHarness adds programmable plug-in components around existing environments so their behavior can be reshaped through standard interfaces. Its companion system, EnvRigger, watches black-box policy rollouts, diagnoses weaknesses, generates targeted components, and validates them with fresh rollouts. Across five benchmarks in four domains, the authors report up to a 9.0-point held-out improvement with 9.8% fewer execution steps. They also argue the setup gives reinforcement learning a stronger optimization signal by letting the policy and environment co-evolve.
HF Daily Papers' note
The paper says EnvHarness adds programmable plug-in components around existing environments so their behavior can be reshaped through standard interfaces. Its companion system, EnvRigger, watches black-box policy rollouts, diagnoses weaknesses, generates targeted components, and validates them with fresh rollouts. Across five benchmarks in four domains, the authors report up to a 9.0-point held-out improvement with 9.8% fewer execution steps. They also argue the setup gives reinforcement learning a stronger optimization signal by letting the policy and environment co-evolve.
HF Daily Papers' note
score 5