Interactive theorem provers (ITPs) such as Lean expose proof construction as a sequence of tactics applied to proof states. Existing machine learning approaches typically treat tactics either as surface tokens or as labels conditioned on the current state, eliding their operator-like semantics. This paper introduces a representation learning framework in which tactics are characterized by the changes they induce on proof states. Using a stepwise Lean proof corpus, we construct delta contexts—token-level additions/removals and typed structural edits—and train simple distributional models (𝛥-SGNS and CBOW-𝛥) to learn tactic embeddings grounded in these state transitions. Experiments on tactic retrieval and operator-style analogy tests show that 𝛥-supervision yields more interpretable and generalizable embeddings than surface-only baselines. Our findings suggest that capturing the semantics of tactics requires modeling their state-transformational effects, rather than relying on distributional co-occurrence alone.