The field of reasoning-oriented reinforcement learning is moving towards more efficient and effective methods for training large language models. Recent works have focused on developing innovative approaches to curriculum learning, such as online difficulty filtering and adaptive curriculum learning, which have shown significant improvements in sample efficiency and training time. Additionally, there is a growing interest in mitigating bias in language models through reasoning-guided fine-tuning. Noteworthy papers in this area include:

• Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning, which introduces a balanced online filtering method that maximizes the effectiveness of RORL training.
• Synthetic Data Generation & Multi-Step RL for Reasoning & Tool Use, which proposes a step-wise reinforcement learning approach that outperforms baseline methods by a significant margin.
• Efficient Reinforcement Finetuning via Adaptive Curriculum Learning, which introduces an adaptive curriculum learning method that improves both the efficiency and final accuracy of reinforcement finetuning.
• Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-Tuning, which shows that improved reasoning capabilities can mitigate harmful stereotypical responses in language models.
• Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization, which proposes a fully unsupervised method for incentivizing reasoning capabilities in large language models.