Reasoning

Abstract: Reinforcement learning (RL) has shown great promise in enhancing LLM reasoning, but current approaches mainly focus on single domains with verifiable rewards. We propose RGR-GRPO, a rubric-driven RL framework for multi-domain reasoning that uses rubrics to provide fine-grained reward signals and offline guidance.

The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding …

Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in …

In this work, we propose a Dynamically Adaptive Density Control Strategy based on the degree of reconstruction of the background of the scene, which adaptive the spatial sample point generation strategy dynamically according to the training results and prevents the generation of redundant data in the model.

Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical …

TBD

作者提出CoT-Influx方法，一种对CoT的示例和内容进行优化从而提高LLMs推理能力的方法，其核心思想是通过剪枝最大化有效信息的输入。

本文提出了一种新的方法，全面评估大模型多个候选答案的可信度，以减轻大模型对于错误答案的过度自信。

3月最新用于游戏的大模型Agent综述

在过去CoT有两种范式，一种是Zero-shot，在问题末尾添加"Let’s think step by step"。另一种Manual CoT(Few-shot CoT)，每个例子由问题和推理链组成。第二种方法表现是否好取决于CoT写的好不好，不过这需要人手工来写。本文通过提出Auto-CoT这一方法使得Few-shot CoT可以自动生成，解放双手！