3 citations · 8 across the 8 of their papers we have counts for
8 papers
LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoning
Weizhe Chen, Sven Koenig, Bistra Dilkina
Since the release of Deepseek-R1, reinforcement learning with verifiable rewards (RLVR) has become a central approach for training large language models (LLMs) on reasoning tasks.…
Iterative Deepening Sampling as Efficient Test-Time Scaling
Weizhe Chen, Sven Koenig, Bistra Dilkina
Recent reasoning models, such as OpenAI's O1 series, have demonstrated exceptional performance on complex reasoning tasks and revealed new test-time scaling laws. Inspired by this,…
RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
Weizhe Chen, Sven Koenig, Bistra Dilkina
In the past year, large language models (LLMs) have had remarkable success in domains outside the traditional natural language processing, and their capacity is further expanded in…
No Panacea in Planning: Algorithm Selection for Suboptimal Multi-Agent Path Finding
Weizhe Chen, Zhihan Wang, Jiaoyang Li +2
Since more and more algorithms are proposed for multi-agent path finding (MAPF) and each of them has its strengths, choosing the correct one for a specific scenario that fulfills s…
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
Weizhe Chen, Sven Koenig, Bistra Dilkina
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applic…
Confidence-Based Curriculum Learning for Multi-Agent Path Finding
Thomy Phan, Joseph Driscoll, Justin Romberg +1
A wide range of real-world applications can be formulated as Multi-Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with indivi…
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet
Weizhe Chen, Sven Koenig, Bistra Dilkina
With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation…
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Thomy Phan, Taoan Huang, Bistra Dilkina +1
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Ne…