The field of large language model (LLM) reasoning is moving towards more robust and effective training methods. Researchers are exploring techniques to mitigate the think-answer mismatch in LLM reasoning, such as noise-aware advantage reweighting, and developing novel frameworks that combine symbolic planning with LLMs for high-quality code generation. Another direction is the development of self-evolving curriculum learning frameworks that enable LLMs to learn from initially unsolved hard problems under sparse rewards. Additionally, there is a focus on improving exploration strategies in reinforcement learning with verifiable rewards (RLVR) and developing heterogeneous multi-expert mutual learning frameworks to address reward sparsity. Notable papers in this area include: Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting, which proposes a principled enhancement to stabilize training. Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization, which formulates prompt selection as a sequential decision process guided by MCTS. EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning, which proposes a self-evolving curriculum learning framework based on two-stage chain-of-thought reasoning optimization. MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement, which utilizes diverse expert prompts and inter-expert mutual learning to boost performance. KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems, which introduces a novel AutoML framework with dynamic solution space exploration.