LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks

arXiv:2603.00540v1 Announce Type: new
Abstract: The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise state-transition objectives. However, this paradigm is bottlenecked by data scarcity, as existing tool-centric reverse-synthesis pipelines fail to capture the rigorous logic of real-world applications. We introduce textbf{LOGIGEN}, a logic-driven framework that synthesizes verifiable training data based on three core pillars: textbf{Hard-Compiled Policy Grounding}, textbf{Logic-Driven Forward Synthesis}, and textbf{Deterministic State Verification}. Specifically, a Triple-Agent Orchestration is employed: the textbf{Architect} compiles natural-language policy into database constraints to enforce hard rules; the textbf{Set Designer} initializes boundary-adjacent states to trigger critical policy conflicts; and the textbf{Explorer} searches this environment to discover causal solution paths. This framework yields a dataset of 20,000 complex tasks across 8 domains, where validity is strictly guaranteed by checking exact state equivalence. Furthermore, we propose a verification-based training protocol where Supervised Fine-Tuning (SFT) on verifiable trajectories establishes compliance with hard-compiled policy, while Reinforcement Learning (RL) guided by dense state-rewards refines long-horizon goal achievement. On $tau^2$-Bench, LOGIGEN-32B(RL) achieves a textbf{79.5% success rate}, substantially outperforming the base model (40.7%). These results demonstrate that logic-driven synthesis combined with verification-based training effectively constructs the causally valid trajectories needed for next-generation agents.