Author name cluster

So Kuroki

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5 papers

2 author rows

ICLR Conference 2025 Conference Paper

Agent Skill Acquisition for Large Language Models via CycleQD

So Kuroki
Taishi Nakamura
Takuya Akiba
Yujin Tang

Training large language models to acquire specific skills remains a challenging endeavor. Conventional training approaches often struggle with data distribution imbalances and inadequacies in objective functions that do not align well with task-specific performance. To address these challenges, we introduce CycleQD, a novel approach that leverages the Quality Diversity framework through a cyclic adaptation of the algorithm, along with a model merging based crossover and an SVD-based mutation. In CycleQD, each task’s performance metric is alternated as the quality measure while the others serve as the behavioral characteristics. This cyclic focus on individual tasks allows for concentrated effort on one task at a time, eliminating the need for data ratio tuning and simplifying the design of the objective function. Empirical results from AgentBench indicate that applying CycleQD to LLAMA3-8B-INSTRUCT based models not only enables them to surpass traditional fine-tuning methods in coding, operating systems, and database tasks, but also achieves performance on par with GPT-3.5-TURBO, which potentially contains much more parameters, across these domains. Crucially, this enhanced performance is achieved while retaining robust language capabilities, as evidenced by its performance on widely adopted language benchmark tasks. We highlight the key design choices in CycleQD, detailing how these contribute to its effectiveness. Furthermore, our method is general and can be applied to image segmentation models, highlighting its applicability across different domains.

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NeurIPS Conference 2025 Conference Paper

Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search

Yuichi Inoue
Kou Misaki
Yuki Imajuku
So Kuroki
Taishi Nakamura
Takuya Akiba

Recent advances demonstrate that increasing inference-time computation can significantly boost the reasoning capabilities of large language models (LLMs). Although repeated sampling (i. e. , generating multiple candidate outputs) is a highly effective strategy, it does not leverage external feedback signals for refinement, which are often available in tasks like coding. In this work, we propose Adaptive Branching Monte Carlo Tree Search (AB-MCTS), a novel inference-time framework that generalizes repeated sampling with principled multi-turn exploration and exploitation. At each node in the search tree, AB-MCTS dynamically decides whether to ''go wider'' by expanding new candidate responses or ''go deeper'' by revisiting existing ones based on external feedback signals. We evaluate our method on complex coding and engineering tasks using frontier models. Empirical results show that AB-MCTS outperforms both repeated sampling and standard MCTS, underscoring the importance of combining the response diversity of LLMs with multi-turn solution refinement for effective inference-time scaling.

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ICRA Conference 2024 Conference Paper

GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy

So Kuroki
Jiaxian Guo
Tatsuya Matsushima
Takuya Okubo
Masato Kobayashi 0001
Yuya Ikeda
Ryosuke Takanami
Paul Yoo

Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world. To address this issue, we introduce GenDOM, a framework that allows the manipulation policy to handle different deformable objects with only a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable object parameters and training it with a diverse range of simulated deformable objects so that the policy can adjust actions based on different object parameters. At the time of inference, given a new object, GenDOM can estimate the deformable object parameters with only a single real-world demonstration by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations in a differentiable physics simulator. Empirical validations on both simulated and real-world object manipulation setups clearly show that our method can manipulate different objects with a single demonstration and significantly outperforms the baseline in both environments (a 62% improvement for in-domain ropes and a 15% improvement for out-of-distribution ropes in simulation, as well as a 26% improvement for ropes and a 50% improvement for cloths in the real world), demonstrating the effectiveness of our approach in one-shot deformable object manipulation. https://sites.google.com/view/gendom/home.

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IROS Conference 2024 Conference Paper

Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning

Hsu-Shen Liu
So Kuroki
Tadashi Kozuno
Wei-Fang Sun
Chun-Yi Lee

This paper explores leveraging the vast knowledge encoded in Large Language Models (LLMs) to tackle pattern formation challenges for swarm robotics systems. A new framework, named LGPF (Language-Guided Pattern Formation), is proposed to address these challenges. The framework breaks down the pattern formation into two key components: pattern synthesis and swarm robotics control. For the former, this study utilizes the exceptional few-shot generalizability of LLMs to translate high-level natural language descriptions into the desired spatial pattern coordinates. This approach allows for overcoming previous limitations in representing and designing complex patterns. The framework further employs a centralized training with decentralized execution (CTDE) based multiagent reinforcement learning (MARL) approach to control the swarm robots in forming the specified pattern while avoiding collisions. The decentralized policies learned with the CTDE-based MARL algorithm consider coordination between robots without direct communication under a partially observable setup. To validate the effectiveness of our framework, we perform extensive experiments in both simulation and real-world environments. These experiments validate LGPF’s effectiveness in accurately and safely forming diverse user-specified patterns.

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IROS Conference 2024 Conference Paper

Multi-Agent Behavior Retrieval: Retrieval-Augmented Policy Training for Cooperative Push Manipulation by Mobile Robots

So Kuroki
Mai Nishimura
Tadashi Kozuno

Due to the complex interactions between agents, learning multi-agent control policy often requires a prohibitive amount of data. This paper aims to enable multi-agent systems to effectively utilize past memories to adapt to novel collaborative tasks in a data-efficient fashion. We propose the Multi-Agent Coordination Skill Database, a repository for storing a collection of coordinated behaviors associated with key vectors distinctive to them. Our Transformer-based skill encoder effectively captures spatio-temporal interactions that contribute to coordination and provides a unique skill representation for each coordinated behavior. By leveraging only a small number of demonstrations of the target task, the database enables us to train the policy using a dataset augmented with the retrieved demonstrations. Experimental evaluations demonstrate that our method achieves a significantly higher success rate in push manipulation tasks compared with baseline methods like few-shot imitation learning. Furthermore, we validate the effectiveness of our retrieve-and-learn framework in a real environment using a team of wheeled robots.

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