EFFECT-GUIDED STRUCTURAL GROWTH IN PRETRAINED SKILL SPACES FOR LIFELONG REINFORCEMENT LEARNING

Expandable representations can support lifelong reinforcement learning, but task failure alone does not reveal whether the active representation is the bottleneck or where additional capacity should be exposed. We introduce Effect-Guided Structural Skill Growth (EGSG), which treats the active span of a pretrained executable skill representation as geometry that can be interrogated online. For each policy-requested short-horizon effect, EGSG measures support mismatch and maps the request through a source-trained inverse model to one candidate latent realization. Persistent out-of-span residuals propose directions to test; they are not treated as proofs of global non-representability. A direction is committed only when held-out execution shows that exposing it reduces matched effect-realization error relative to both the current span and a source-covariance-matched alternative. On Meta-World MT10 and a 12-task MT50 lifelong stream, EGSG reaches 0.78 and 0.66 final average success, respectively; on MT10, it accepts only 2.4 structural updates on average while outperforming interface-matched fixed-capacity and comparable-growth controls. Across the 24 accepted MT10 updates, the proposed directions align with held-out expert residual structure above matched nulls, reduce normalized out-of-span residuals by 19% and matched effect error by 27%, and improve matched success by 27 percentage points before subsequent policy optimization. No post-hoc false-growth event is observed among these 24 accepted updates; removing execution validation reintroduces such events. A five-task real-robot stream provides a preliminary feasibility demonstration.