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Abstract:Despite the strong reasoning capabilities of massive language fashions (LLMs), optimizing the execution efficiency of tensor programs stays challenging resulting from the need for precise, composable transformation selections. Recent LLM-guided approaches body tensor program optimization as an iterative choice course of, however current datasets present solely finish-to-finish optimized program pairs using token-inefficient representations, lacking verifiable step-level supervision and interpretability. agreement in principle consequence, LLMs battle to make reliable single-step choices in massive combinatorial optimization areas. We introduce Step-TP, a submit-coaching dataset for tensor program optimization that gives grounded, atomic, step-stage supervision with structured chain-of-thought (CoT) reasoning. Step-TP kinds a closed reasoning loop over intermediate program states, enabling dependable multi-step optimization moderately than end result imitation. Its design is guided by four rules: (i) a token-efficient, verifiable intermediate illustration (IR) that deterministically lowers to TVM TIR; (ii) atomic and composable optimization strategies that decompose advanced trajectories into interpretable single-step selections; (iii) structured CoT supervision coupled with explicit IR-to-IR state transitions; and (iv) technique filtering to stability protection while preventing shortcut exploitation. The dataset and implementation can be found at a GitHub link, this https URL.
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