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Abstract:Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution effectivity of tensor applications stays challenging attributable to the need for exact, composable transformation decisions. Recent LLM-guided approaches frame tensor program optimization as an iterative decision process, but present datasets provide only end-to-finish optimized program pairs utilizing token-inefficient representations, lacking verifiable step-stage supervision and interpretability. Because of this, LLMs struggle to make dependable single-step choices in giant combinatorial optimization areas. We introduce Step-TP, a put up-training dataset for tensor program optimization that provides grounded, atomic, step-level supervision with structured chain-of-thought (CoT) reasoning. Step-TP varieties a closed reasoning loop over intermediate program states, enabling reliable multi-step optimization moderately than final result imitation. Its design is guided by four rules: (i) a token-efficient, verifiable intermediate representation (IR) that deterministically lowers to TVM TIR; (ii) atomic and composable optimization methods that decompose complex trajectories into interpretable single-step selections; (iii) structured CoT supervision coupled with explicit IR-to-IR state transitions; and (iv) strategy filtering to stability coverage while preventing shortcut exploitation. The dataset and implementation are available at a GitHub link, this https URL.

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