✨RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

📝 Summary:
RLVE improves language model reasoning by dynamically adjusting problem difficulty in verifiable environments. This adaptive approach significantly outperforms static environments and traditional RL, yielding a 3.37% average improvement on reasoning benchmarks.

🔹 Publication Date: Published on Nov 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07317
• PDF: https://arxiv.org/pdf/2511.07317
• Github: https://github.com/Zhiyuan-Zeng/RLVE

🔹 Models citing this paper:
• https://huggingface.co/hamishivi/Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE
• https://huggingface.co/hamishivi/OpenThinker3-1.5B-RLVE

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#ReinforcementLearning #LLMs #AI #AIReasoning #AdaptiveLearning