Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning

• URL: http://arxiv.org/abs/2602.13218v1
• Date: Fri, 23 Jan 2026 13:26:01 GMT
• Title: Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
• Abstract summary: SSLogic is a framework for continuous family evolution with controllable difficulty.<n>Training on SSLogic-evolved data yields consistent gains over the seed baseline at matched steps.
• Score: 18.75349680577575
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Scaling verifiable training signals remains a key bottleneck for Reinforcement Learning from Verifiable Rewards (RLVR). Logical reasoning is a natural substrate: constraints are formal and answers are programmatically checkable. However, prior synthesis pipelines either depend on expert-written code or operate within fixed templates/skeletons, which limits growth largely to instance-level perturbations. We propose SSLogic, an agentic meta-synthesis framework that scales at the task-family level by iteratively synthesizing and repairing executable Generator--Validator program pairs in a closed Generate--Validate--Repair loop, enabling continuous family evolution with controllable difficulty. To ensure reliability, we introduce a Multi-Gate Validation Protocol that combines multi-strategy consistency checks with Adversarial Blind Review, where independent agents must solve instances by writing and executing code to filter ambiguous or ill-posed tasks. Starting from 400 seed families, two evolution rounds expand to 953 families and 21,389 verifiable instances (from 5,718). Training on SSLogic-evolved data yields consistent gains over the seed baseline at matched training steps, improving SynLogic by +5.2, BBEH by +1.4, AIME25 by +3.0, and Brumo25 by +3.7.

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