Tool-integrated reasoning has emerged as a promising paradigm for Text-to-SQL, framing SQL generation as sequential decision-making interleaved with tool execution. However, existing reinforcement learning approaches rely on coarse-grained outcome supervision, creating a credit assignment problem: models receive identical rewards regardless of whether intermediate steps are redundant or erroneous. We observe that tool-integrated reasoning offers a unique opportunity: each tool invocation produces structured, verifiable outputs that can serve as training-free step-level signals, eliminating the need for expensive reward model training. Building on this insight, we propose FineStep, a step-level credit assignment framework that leverages training-free process rewards derived from tool execution feedback. FineStep constructs a dual-channel value estimate for each step, combining backward-propagated outcome value with forward-smoothed process value, and synthesizes them via group-based normalization into a unified step-level advantage for policy optimization. Experiments on Text-to-SQL benchmarks show that FineStep achieves SOTA performance with a 3.25% EX gain over GRPO at the 4B scale, while results on AIME24 demonstrate transferability beyond SQL.

Developing an efficient retriever to retrieve knowledge from a large-scale knowledge base (KB) is critical for task-oriented dialogue systems to effectively handle localized and specialized tasks. However, widely used generative models such as T5 and ChatGPT often struggle to differentiate subtle differences among the retrieved KB records when generating responses, resulting in suboptimal quality of generated responses. In this paper, we propose the application of maximal marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. In addition, our approach goes beyond considering solely retrieved entities and incorporates various meta knowledge to guide the generator, thus improving the utilization of knowledge. We evaluate our approach on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses. The code of this work is available at https://github.com/shenwzh3/MK-TOD.