CVPO: Enhancing LLM Reinforcement Learning Reasoning via Value-Variance Adaptation and Dynamic Curriculum Learning
Ziqi Jia, Yalu Ouyang, Bo Pang, Panpan Li, Hangfei Xu, Shengzhao Wen, Shiyong Li, Yanpeng Wang
cs.CL, cs.AI, cs.LG
Submitted: 2026-08-04
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
- Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
- DeepSeek-V3 Technical Report
- REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization
- Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
- Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
- Maximizing Confidence Alone Improves Reasoning
- Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models?
- Offline Regularised Reinforcement Learning for Large Language Models Alignment
- Proximal Policy Optimization Algorithms
- Spurious Rewards: Rethinking Training Signals in RLVR
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Efficient Reinforcement Finetuning via Adaptive Curriculum Learning
- DGRO: Enhancing LLM Reasoning via Exploration-Exploitation Control and Reward Variance Management
- Kimi k1.5: Scaling Reinforcement Learning with LLMs
- DAPO: An Open-Source LLM Reinforcement Learning System at Scale
- What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret
- VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
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