提出超越正确性、奖励推理忠实性的RAG方法，为AI对齐提供新思路。
arXiv:2510.13272v3 Announce Type: replace Abstract: Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for …
从强化学习视角出发，揭示LLM奖励机制与社会法规的结构性相似，引发深层思考
arXiv:2606.04075v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language mo…
把推荐计划直接嵌入URL缩短器，追踪转化并支持固定或比例奖励，省去多平台切换的麻烦。
This article was originally published on Jo4 Blog . Every SaaS founder eventually asks the same question: "How do I get my existing users to bring me …
将元认知原理引入LLM推理强化，用知识和调节信号作为奖励，探索提升模型推理能力的新路径。
arXiv:2605.23384v1 Announce Type: cross Abstract: Recent RL methods have substantially improved the reasoning abilities of LLMs. Existing reward desig…
一个挑战经典认知的开源项目：澄清奖励与强化的真正关系，引发RL领域新思考。
Article URL: https://github.com/nikitph/rewarder Comments URL: https://news.ycombinator.com/item?id=48265835 Points: 1 # Comments: 0
用分布感知奖励替代点估计，强化学习让LLM回归预测分布更校准
arXiv:2605.20740v1 Announce Type: new Abstract: Large language models can predict real-valued quantities from heterogeneous inputs such as text, code,…
提出逐步评分奖励机制，优化LLM推理的中间步骤监督，突破传统仅奖励最终答案的局限。
arXiv:2605.17291v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large lan…