面向长时程智能体，TRCA用过渡级评分信用分配破解稀疏奖励难题，值得关注。
arXiv:2608.16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, …
DeepMind的AlphaZero在稀疏奖励游戏中表现有限，论文提出辅助监督方法提升性能。
arXiv:2607.08984v1 Announce Type: cross Abstract: AlphaZero has demonstrated that a neural-guided Monte Carlo Tree Search can achieve superhuman perfo…
用探索性强化学习改善LLM中间训练，解决基模型覆盖不足问题，提升推理能力。
arXiv:2606.17024v1 Announce Type: new Abstract: Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but …
让大模型在稀疏奖励环境中引导强化学习策略，通过不确定性估计提升决策可靠性，有代码可复现。
arXiv:2606.06673v1 Announce Type: new Abstract: Sparse rewards and heterogeneous task sequences remain persistent challenges in Reinforcement Learning…
提出稀疏到稠密奖励原则，四阶段后训练流程更高效利用稀缺标注数据，为LLM推理优化提供新范式。
arXiv:2605.12483v2 Announce Type: replace-cross Abstract: When labeled verifiable training data is scarce, each checked example should be used where i…
速览强化学习稀疏奖励的半监督解决方案，来自arXiv最新研究
arXiv:2501.19128v5 Announce Type: replace-cross Abstract: In many real-world scenarios, reward signal for agents are exceedingly sparse, making it cha…