Why LLMs still fail at chess may be a key test of how capabilities emerge
alex_peys · x · 2026-07-29
The post argues that chess is still a useful task for studying LLMs.
- Commercial LLMs still perform poorly at chess despite likely having seen huge amounts of chess material in pretraining.
- The author notes that direct SFT/RL on chess would obviously make models much stronger, and even small networks can reach a very high level with a short training run.
- The open question is whether chess ability can be extracted without brute-force task-specific verified rewards or behavior cloning.
Related event: LLMs Still Struggle with Chess Despite Pre-training(3 posts)→
More from Research
- Tsinghua professor's ApexIntelligence raises $50M to build self-evolving foundation models — DeryaTR_ · 2026-10-16
- Anders Sandberg on fluid computation: error budgets tighten quadratically over time — anderssandberg · 2026-10-16
- How the fluid computation works: force field advects particle to 0/1 boxes — anderssandberg · 2026-10-16
- FlashAttention creator Tri Dao joins Radical Numerics as scientific advisor — exnx · 2026-10-16
- SAIR Launches Open Math OS, Offering Free Compute and 100+ Tools for Community-Owned Math AI — Thom_Wolf · 2026-10-16
- JapanFold Claims One Tenstorrent Server Beats an Nvidia DGX B200 on Bio Models at a Third of the Price — DavidBennett__ · 2026-10-16