Zhechong Huang

Zhechong Huang, Zhao Zhang, Zeyu Sun et al.

The specification synthesis task aims to automatically generate specifications, together with any necessary auxiliary verification annotations, for existing programs. This task is important because such specifications serve as behavioral contracts that support modular reasoning and reusable verification across a codebase. At the same time, it remains challenging because verifier-only feedback is fundamentally incomplete: passing verification establishes soundness, but cannot distinguish weak specifications from strong ones. What is missing is a fine-grained signal for specification completeness. We present SpecRL, a reinforcement learning framework for specification synthesis in Dafny. SpecRL introduces a self-contained pipeline that generates negative tests, i.e., input-output pairs that can never be produced by the program. We use the fraction of these negative tests rejected by a candidate specification as a signal of specification completeness, which is integrated into the reward for RL training. Experiments across four model sizes show that SpecRL improves both specification strength and verification success over SFT and RL with a binary specification-strength reward, generalizes to an out-of-distribution benchmark, and remains competitive on that unseen benchmark compared to much larger general-purpose LLMs.

Zhechong Huang, Zhao Zhang, Ruyi Ji et al.

Language models have shown remarkable proficiency in code generation; nevertheless, ensuring type correctness remains a challenge. Although traditional methods, such as constrained decoding, alleviate this problem by externally rejecting untypable code, the model itself does not effectively learn type reasoning internally, which ultimately limits its overall performance. This paper introduces TyFlow, a novel system that internalizes type reasoning within code generation to guide the model to learn the type system. The core of our approach is a novel type-guided program synthesis system that maintains an isomorphism between type derivation trees and synthesis derivation trees, enabling a new code representation based on synthesis decision sequences rather than traditional text-based token sequences. By offloading the complexity of type system learning to the representation itself, models can redirect their computational resources toward higher-level program semantics. Our evaluation shows that TyFlow not only eliminates type errors but also significantly improves functional correctness, highlighting the importance of aligning LMs with type systems internally.