ExpSeek: Self-Triggered Experience Seeking for Web Agents

Recent advancements in multilingual reasoning models have been highlighted with the introduction of Language-Mixed Chain-of-Thought (CoT), which utilizes English as an anchor to enhance reasoning in other languages, specifically Korean. The study presents the KO-REAson-35B model, which achieved state-of-the-art performance in reasoning tasks, supported by a curated dataset of Korean prompts known as Yi-Sang.

ToolRM has been introduced as a new family of lightweight reward models specifically designed for tool-use scenarios, addressing the limitations of existing reward models in aligning large language models (LLMs) with human preferences. This development includes a novel pipeline for generating high-quality preference data and a benchmark for evaluating these models on tool-calling tasks.

KVzap has been introduced as a fast and adaptive method for key-value (KV) cache pruning in transformer-based language models, addressing the critical inference bottleneck caused by growing context lengths. This method achieves 2-4 times KV cache compression with minimal accuracy loss, demonstrating state-of-the-art performance on the KVpress leaderboard.