2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
Scaling LLM Inference with Optimized Sample Compute Allocation
Kexun Zhang, Shang Zhou, Danqing Wang +2
Sampling is a basic operation in many inference-time algorithms of large language models (LLMs). To scale up inference efficiently with a limited compute, it is crucial to find an…
Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models
Danqing Wang, Zhuorui Ye, Fei Fang +1
Enhancing the reasoning capabilities of large language models (LLMs) is crucial for enabling them to tackle complex, multi-step problems. Multi-agent frameworks have shown great po…
Revealing the Barriers of Language Agents in Planning
Jian Xie, Kexun Zhang, Jiangjie Chen +5
Autonomous planning has been an ongoing pursuit since the inception of artificial intelligence. Based on curated problem solvers, early planning agents could deliver precise soluti…
TypedThinker: Diversify Large Language Model Reasoning with Typed Thinking
Danqing Wang, Jianxin Ma, Fei Fang +1
Large Language Models (LLMs) have demonstrated strong reasoning capabilities in solving complex problems. However, current approaches primarily enhance reasoning through the elabor…