用智能体自动生成逻辑推理数据族，提升强化学习训练效果。
Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning
- 智能体迭代生成并优化生成-验证代码对，实现任务家族的自主演化。
- 从400个种子家族进化出953个新家族，共21,389个可验证实例。
- 在Enigmata等数据集上，逻辑与操作能力分别提升13.2%和9.6%。
强化学习中基于可验证奖励（RLVR）面临数据瓶颈：现有合成流程依赖专家编写的代码或固定模板，仅能进行实例级微调。本文提出SSLogic框架，将可演化单元从问题实例升级为任务家族规范。该框架通过大模型智能体在生成-验证-优化闭环中持续编写与改进可执行的生成器-验证器对，产出具有新规则和难度梯度的任务家族，而非旧有任务的参数变体。采用多门控验证协议——多策略共识结合对抗盲审，由独立智能体编写并执行代码求解每个实例，过滤无效任务。初始400个种子家族经两轮演化，生成953个家族及21,389个可验证实例。三组对比（步数匹配、词元匹配、规模控制）在外部Enigmata数据上一致显示演化数据训练效用更高，分别在SynLogic、AIME25、BBH上取得+5.2、+3.0、+5.5的提升。细粒度KORBench评估显示逻辑能力提升13.2%，操作能力提升9.6%，证实结构演化与下游性能提升的关联性。
原文摘要 · Abstract (English)
Reinforcement Learning from Verifiable Rewards (RLVR) is bottlenecked by data: existing synthesis pipelines rely on expert-written code or fixed templates, confining growth to instance-level perturbations. We shift the evolvable unit from problem instances to task-family specifications. SSLogic is an agentic meta-synthesis framework in which LLM agents iteratively author and refine executable Generator-Validator pairs inside a closed Generate-Validate-Refine loop, producing families with new rules and difficulty gradients rather than parameter variations of old ones. A Multi-Gate Validation Protocol -- multi-strategy consensus plus Adversarial Blind Review, where independent agents solve each instance by writing and executing code -- filters ill-posed tasks before they enter training. Starting from 400 seed families, two evolution rounds yield 953 families and 21,389 verifiable instances. Three converging comparisons (step-matched, token-matched, and size-controlled on external Enigmata data) consistently show higher training utility of evolved data, with gains of SynLogic +5.2, AIME25 +3.0, and BBH +5.5 on Enigmata. Fine-grained KORBench evaluation reveals selective improvements in logic (+13.2%) and operation (+9.6%), linking structural evolution to downstream gains. Code: https://github.com/AdAstraAbyssoque/Scaling-the-Scaling-Logic
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