Recent advances in text-to-SQL have shown that methods such as Group Relative Policy Optimization (GRPO) can substantially improve reasoning performance, but these approaches remain inherently on-policy, limiting their ability to incorporate novel reasoning patterns. In this work, we address this limitation by leveraging existing datasets to generate high-quality off-policy rollouts, enabling mixed-policy training that exposes models to diverse and informative reasoning trajectories. We present the first application of mixed-policy GRPO to the text-to-SQL domain and introduce a systematic study of off-policy data generation strategies for this setting, including a novel method, Iterative Error Correction (IEC), which iteratively refines model outputs through targeted feedback. Our experiments show that mixed-policy GRPO outperforms both base models and on-policy GRPO, yielding average improvements of +4.7% over base models and +4.1% over on-policy GRPO across the Spider and BIRD benchmarks. Gains are particularly strong on BIRD, reaching up to +7.3% over base models and +4.5% over on-policy GRPO.

Reasoning benchmarks have played a crucial role in the progress of language models. Yet rigorous evaluation remains a significant challenge as static question-answer pairs provide only a snapshot of performance, compressing complex behavior into a single accuracy metric. This limitation is especially true in complex, rule-bound domains such as law, where existing benchmarks are costly to build and ill suited for isolating specific failure modes. To address this, we introduce OpenExempt, a framework and benchmark for diagnostic evaluation of legal reasoning. The OpenExempt Framework uses expert-crafted symbolic representations of U.S. Bankruptcy Code statutes to dynamically generate a large space of natural language reasoning tasks and their machine-computable solutions on demand. This gives users fine-grained control over task complexity and scope, allowing individual reasoning skills to be probed in isolation. Using this system, we construct the OpenExempt Benchmark, a diagnostic benchmark for legal reasoning with 9,765 samples across nine evaluation suites designed to carefully probe model capabilities. Experiments on 13 diverse language models reveal sharp performance cliffs that emerge only under longer reasoning paths and in the presence of obfuscating statements. We release the framework and benchmark publicly to support research aimed at understanding and improving the next generation of reasoning systems.

Large language models (LLMs) are capable of producing documents, and retrieval augmented generation (RAG) has shown itself to be a powerful method for improving accuracy without sacrificing fluency. However, not all information can be retrieved from text. We propose an approach that uses the analysis of structured data to generate fact sets that are used to guide generation in much the same way that retrieved documents are used in RAG. This analytics augmented generation (AAG) approach supports the ability to utilize standard analytic techniques to generate facts that are then converted to text and passed to an LLM. We present a neurosymbolic platform, Satyrn, that leverages AAG to produce accurate, fluent, and coherent reports grounded in large scale databases. In our experiments, we find that Satyrn generates reports in which over 86% of claims are accurate while maintaining high levels of fluency and coherence, even when using smaller language models such as Mistral-7B, as compared to GPT-4 Code Interpreter in which just 57% of claims are accurate.