# CoRPO: Adding a Correctness Bias to GRPO Improves Generalization

arXiv: https://arxiv.org/abs/2511.04439 · HTML: https://mlsanity.com/paper/2511.04439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning.
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Placed in

- LLM reasoning / [Reinforcement learning for reasoning](https://mlsanity.com/lr/reinforcement-learning-for-reasoning/)

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Understanding R1-Zero-Like Training: A Critical Perspective](https://mlsanity.com/paper/2503.20783/) (2025)
- [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://mlsanity.com/paper/2501.12599/) (2025)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [Inference-Time Scaling for Generalist Reward Modeling](https://mlsanity.com/paper/2504.02495/) (2025)
- [The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning](https://mlsanity.com/paper/2506.01347/) (2025)
- [TACO: Topics in Algorithmic COde generation dataset](https://mlsanity.com/paper/2312.14852/) (2023)
- [The Art of Scaling Reinforcement Learning Compute for LLMs](https://mlsanity.com/paper/2510.13786/) (2025)
- [AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning](https://mlsanity.com/paper/2505.16400/) (2025)
- [Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening](https://mlsanity.com/paper/2506.02355/) (2025)
- [Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective](https://mlsanity.com/paper/2510.10150/) (2025)
- [The Role of Baselines in Policy Gradient Optimization](https://mlsanity.com/paper/2301.06276/) (2023)
- [RL Grokking Recipe: How Does RL Unlock and Transfer New Algorithms in LLMs?](https://mlsanity.com/paper/2509.21016/) (2025)
- [Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning](https://mlsanity.com/paper/2510.03669/) (2025)

## Built on by (most cited first, 1 of 1)

- [OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents](https://mlsanity.com/paper/2605.05185/) (2026)

## Abstract (arXiv)

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 behaviours. 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.