Testing a Zero-Parameter Model Against KataGo
Summary
Tests a zero-parameter model against KataGo, evaluating its performance in the game of Go.
Similar Articles
GoBench: Evaluating LLMs on the game of Go [R]
GoBench is a benchmark for evaluating large language models on 9x9 Go games, demonstrating strong correlation with ARC-AGI and featuring a leaderboard with KataGo opponents from random to superhuman levels.
WallZero: Mastering the Game of WallGo with Strategic Analysis
This paper presents WallZero, an AlphaZero-based agent for the two-player board game WallGo, which defeats professional Go players and is used to analyze game balance and strategies.
G-Zero: Self-Play for Open-Ended Generation from Zero Data
This paper introduces G-Zero, a verifier-free framework that enables autonomous large language model self-improvement through co-evolutionary training using intrinsic rewards and hint-based guidance. It aims to overcome the limitations of proxy LLM judges in open-ended tasks by deriving supervision from internal distributional dynamics.
Go grandmaster Shin defeats AI KataGo with a two-stone handicap
Go grandmaster Shin Jin-seo defeated AI engine KataGo in a historic three-match series, marking the first human victory against a state-of-the-art Go AI under a two-stone handicap.
J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data
J-Zero is a unified framework for co-evolving Challenger, Solver, and Judge models from zero data, enabling self-improvement in language models across both verifiable and unverifiable domains with performance surpassing baselines.