47 citations · 54 across the 9 of their papers we have counts for
9 papers
Discovering Novel LLM Experts via Task-Capability Coevolution
Andrew Dai, Boris Meinardus, Ciaran Regan +2
Frontier model developers aim to train models continually to possess emergent, diverse capabilities. To extend capabilities, the current pre-training and post-training paradigm req…
Shachi: A Modular, Controllable Framework for LLM-Based Agent-Based Modeling of Emergent Collective Behavior
So Kuroki, Yingtao Tian, Kou Misaki +3
How collective behaviors emerge from the interactions of individual LLM-driven agents is a central question in artificial life, yet controlled study of these emergent dynamics has…
Evolution of Collective AI Beyond Individual Optimization
Ryosuke Takata, Yujin Tang, Yingtao Tian +3
This study investigates collective behaviors that emerge from a group of homogeneous individuals optimized for a specific capability. We created a group of simple, identical neural…
Agent Skill Acquisition for Large Language Models via CycleQD
So Kuroki, Taishi Nakamura, Takuya Akiba +1
Training large language models to acquire specific skills remains a challenging endeavor. Conventional training approaches often struggle with data distribution imbalances and inad…
Position: Leverage Foundational Models for Black-Box Optimization
Xingyou Song, Yingtao Tian, Robert Tjarko Lange +3
Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fi…
Evolution Transformer: In-Context Evolutionary Optimization
Robert Tjarko Lange, Yingtao Tian, Yujin Tang
Evolutionary optimization algorithms are often derived from loose biological analogies and struggle to leverage information obtained during the sequential course of optimization. A…
Evolutionary Optimization of Model Merging Recipes
Takuya Akiba, Makoto Shing, Yujin Tang +2
Large language models (LLMs) have become increasingly capable, but their development often requires substantial computational resources. While model merging has emerged as a cost-e…
Large Language Models As Evolution Strategies
Robert Tjarko Lange, Yingtao Tian, Yujin Tang
Large Transformer models are capable of implementing a plethora of so-called in-context learning algorithms. These include gradient descent, classification, sequence completion, tr…
NeuroEvoBench: Benchmarking Evolutionary Optimizers for Deep Learning Applications
Robert Tjarko Lange, Yujin Tang, Yingtao Tian
Recently, the Deep Learning community has become interested in evolutionary optimization (EO) as a means to address hard optimization problems, e.g. meta-learning through long inne…