5 papers · 1 filter
From Solving to Verifying: A Unified Objective for Robust Reasoning in LLMs
Xiaoxuan Wang, Bo Liu, Song Jiang +4
The reasoning capabilities of large language models (LLMs) have been significantly improved through reinforcement learning (RL). Nevertheless, LLMs still struggle to consistently v…
AI based signage classification for linguistic landscape studies
Yuqin Jiang, Song Jiang, Jacob Algrim +8
Linguistic Landscape (LL) research traditionally relies on manual photography and annotation of public signages to examine distribution of languages in urban space. While such meth…
Large Reasoning Models Learn Better Alignment from Flawed Thinking
ShengYun Peng, Pin-Yu Chen, Eric Smith +6
Large reasoning models (LRMs) "think" by generating structured chain-of-thought (CoT) before producing a final answer, yet they still lack the ability to reason critically about sa…
HGCA: Hybrid GPU-CPU Attention for Long Context LLM Inference
Weishu Deng, Yujie Yang, Peiran Du +6
Scaling inference for large language models (LLMs) is increasingly constrained by limited GPU memory, especially due to growing key-value (KV) caches required for long-context gene…
SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks
Yifei Zhou, Song Jiang, Yuandong Tian +4
Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform…