LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks

TL;DR

LOGIGEN is introduced, a logic-driven framework that synthesizes verifiable training data based on three core pillars: Hard-Compiled Policy Grounding, Logic-Driven Forward Synthesis, and Deterministic State Verification, and a verification-based training protocol where Supervised Fine-Tuning on verifiable trajectories establishes compliance with hard-compiled policy.

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 $τ^2$-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.

Figures (10)

• Figure 1: (a) Performance comparisons. LOGIGEN-32B (RL) achieves a 79.5% success rate on $\tau^2$-Bench, significantly outperforming open-weight baselines and remaining competitive with proprietary models. It also surpasses general-purpose baselines with substantially larger parameters. (b) Performance breakdown. The gains stem from our verification-based training protocol: SFT on verifiable trajectories establishes compliance with hard-compiled policy, while RL guided by deterministic state-rewards refines long-horizon goal achievement.
• Figure 2: LOGIGEN synthesizes verifiable agentic tasks via a Triple-Agent Orchestration: (1) the Architect expands seed domain knowledge into a Wiki Policy and compiles it into a Hard-Compiled Policy Environment; (2) the Set Designer seeds boundary-adjacent initial states ($N{-}1$) to maximize logical friction; and (3) the Explorer performs goal-conditioned exploration to discover executable multi-turn episodes, producing a spoiler-free task description and a deterministic target database snapshot for state-based verification.
• Figure 3: Task complexity profile of LOGIGEN.(a) Distribution of the top-10 complexity tags assigned to generated tasks, showing that most samples involve conditional logic, multi-step interaction, and attribute-based selection, with a substantial portion requiring state-based reasoning and fallback (waterfall) behaviors. (b) Difficulty-level distribution, where L1 (Simple) tasks are rare (51), while the dataset is dominated by L2 (Intermediate; 6,161) and L3 (Advanced; 3,788) tasks, indicating a strong bias toward non-trivial, policy-constrained problem solving.
• Figure 4: Training and Evaluation Protocol of LOGIGEN. The protocol operates on a Data Package containing the Wiki Policy ($\mathcal{P}$), Task Description ($\mathcal{I}$), and paired database snapshots. Within the Interaction Loop, a User Simulator (driven by $\mathcal{I}$) interacts with the Agent (governed by $\mathcal{P}$ and tools in $\mathcal{E}$). Each tool invocation induces a deterministic mutation of the database state ($s_\text{origin} \rightarrow s_\text{1} \rightarrow s_\text{2} \rightarrow \dots \rightarrow s_\text{final}$). The Verifier computes a binary success metric $R_{final}$ by performing a canonicalized State-Diff between the resulting state $s_\text{final}$ and the ground-truth target $s_\text{target}$.
• Figure 5: Training reward curves for LOGIGEN-8B (left) and 32B (right) models. Compared to Vanilla GRPO, TA-GRPO exhibits higher sample efficiency and achieves superior asymptotic rewards, particularly on the 8B scale.