The Vault

Group-Relative Policy Optimization (GRPO) has emerged as the standard for training reasoning capabilities in large language models through reinforcement learning. By estimating advantages using group-mean rewards rather than a learned critic, GRPO has enabled efficient scaling of reinforcement learning from verifiable rewards (RLVR). However, we identify a fundamental limitation: GRPO’s mean baseline can assign positive advantages to incorrect solutions simply because they outperform a poorly-performing group average. It leads to overestimation of advantages and reinforcement of incorrect behaviors. To address this, we propose Correctness-Relative Policy Optimization (CoRPO), a simple modification to the GRPO objective that clips the minimum baseline to a fixed correctness threshold. We show that baseline clipping introduces a protective bias to advantage estimation that mitigates overfitting while preserving effective exploration. Empirically, CoRPO-trained models improve cross-domain reasoning, generalizing more consistently to out-of-domain (OOD) tasks. When trained on coding tasks, CoRPO outperforms GRPO on math, and vice-versa, indicating that CoRPO learns robust, transferable reasoning patterns rather than task-specific solutions.

Large Language Models (LLMs) excel at general tasks but underperform in specialized domains like economics and psychology, which require deep, principled understanding. To address this, we introduce ACER (Automated CurriculumEnhanced Regimen) that transforms generalist models into domain experts without sacrificing their broad capabilities. ACER first synthesizes a comprehensive, textbook-style curriculum by generating a table of contents for a subject and then creating question-answer (QA) pairs guided by Bloom’s taxonomy. This ensures systematic topic coverage and progressively increasing difficulty. The resulting synthetic corpus is used for continual pretraining with an interleaved curriculum schedule, aligning learning across both content and cognitive dimensions. Experiments with Llama 3.2 (1B and 3B) show significant gains in specialized MMLU subsets. In challenging domains like microeconomics, where baselines struggle, ACER boosts accuracy by 5 percentage points. Across all target domains, we observe a consistent macro-average improvement of 3 percentage points. Notably, ACER not only prevents catastrophic forgetting but also facilitates positive cross-domain knowledge transfer, improving performance on non-target domains by 0.7 points. Beyond MMLU, ACER enhances performance on knowledgeintensive benchmarks like ARC and GPQA by over 2 absolute points, while maintaining stable performance on general reasoning tasks. Our results demonstrate that ACER offers a scalable and effective recipe for closing critical domain gaps in LLMs.