REVIEW 2 cited by
Accelerating Self-Play Learning in Go
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carlo tree search self-play. But whereas AlphaZero required thousands of TPUs over several days and ELF required thousands of GPUs over two weeks, KataGo surpasses ELF's final model after only 19 days on fewer than 30 GPUs. Much of the speedup involves non-domain-specific improvements that might directly transfer to other problems. Further gains from domain-specific techniques reveal the remaining efficiency gap between the best methods and purely general methods such as AlphaZero. Our work is a step towards making learning in state spaces as large as Go possible without large-scale computational resources.
Forward citations
Cited by 2 Pith papers
-
When AI Becomes Routine: A Decade of Public AI Mediation in Korean Go Commentary
In late-routine Korean Go commentary, AI source labels recede while winrate/point-gap talk persists, shifting mediation from explicit naming toward interface rendering—more so on creator channels.
-
Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
A disentangled Belief head with uncertainty gating is claimed to replace MCTS correction and enable professional-level search-free Go on consumer GPUs, but the reported experiments do not demonstrate that claim.
Discussion (0). Continue with ORCID to comment.