13-Mar-2026
#AI’s #gamePlaying still has flaws: #AlphaZero-style self-play tested on #Nim
Despite heavy training, agents show blind spots and can miss optimal moves

Games are often called the ‘Formula 1’ of AI: clear rules, clear winners. AlphaZero-style algorithms learn by self-play: a neural network predicts moves and guides tree search. We tested this recipe on Nim, a simple children’s matchstick game that has been mathematically solved. Because the correct move is known for every position, we can measure whether an agent plays optimally across the state space. We find a gap: learning can work on small boards, but blind spots remain and performance degrades as the board grows, with predictions approaching random. This suggests impartial games often need analytic representations, not pattern learning.
NVIDIA AI Developer (@NVIDIAAIDev)
Nemotron 3 Super가 워크스테이션부터 클라우드까지 다양한 환경에 배포 지원되며 API, OpenRouter, build.nvidia.com 경유로 접근 가능하다는 공지입니다. 주요 추론(inference) 플랫폼에서 NVIDIA NIM 패키지로 제공되어 배포와 사용이 용이해졌습니다.

@openclaw @perplexity_ai Ready to get started? Nemotron 3 Super supports deployment across environments, from workstations to the cloud, and can be accessed through API, OpenRouter, or https://t.co/fC1rz1G9c4. It is now live and available on major inference platforms, packaged as NVIDIA NIM: 📥
Nim version 2.2.8 released