Steerable Instruction-Following Code Synthesis via Actor-Schema Co-Evolution

Interpreting and following human instructions is a critical capability of large language models (LLMs) in automatic programming. However, synthesizing large-scale coding data paired with fine-grained, verifiable instructions remains largely unexplored, particularly when ensuring logical compatibility among multiple constraints. This setting reflects real-world user specifications, e.g., coding standards, dependency restrictions, and interface contracts. In this study, we propose IFCodeEvolve, an actor-schema co-evolution framework for instruction-following coding data generation. By representing each instruction as a parametric function schema, we construct a library that covers the vast constraint space via dynamic instantiation. Building upon this, a Monte Carlo Tree Search (MCTS) sampler is applied to efficiently navigate this space, utilizing actor model feedback as a dynamic termination signal. Furthermore, to progressively explore challenging problems, we introduce a co-evolving paradigm that iteratively advances both the actor model and the schema library based on sampler statistics. Empirical results demonstrate that IFCodeEvolve significantly boosts base model performance, with our 32B model achieving parity with proprietary SOTA models. Besides, we contribute IFCodeBench, a comprehensive human-verified benchmark equipped with solutions and robust AST-based verification.