Training Contracts: Learning Validity Boundaries for Reusing Training Interventions in Embodied Reinforcement Learning

Embodied reinforcement learning accumulates experience from training interventions, which may be transferred to future learning processes. However, the same intervention can have different effects under different learning contexts, making it difficult to determine when an intervention remains valid for reuse. We introduce Training Contracts, which learn an explicit validity boundary for each training intervention by comparing training processes with and without each intervention in the source domain. Each contract records supported, contradicted, and unresolved regions, and validates the boundary on independent source checkpoints. For target tasks, the trainer uses the validity boundary to decide whether to reuse an intervention, perform a probe, or abstain. All target policies are trained from scratch, and only external intervention knowledge is transferred. Across twelve target configurations from six task families, Training Contracts improve average performance from 0.59 to 0.64 (8.5% relative gain) over the strongest conditional-effect prediction baseline. Beyond the primary benchmark, including Meta-World and real-robot experiments, Training Contracts improve success rates by 14.5% and 15.1% over the average baseline performance, respectively.