Rishi Shandilya

dark2quasar

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liked a model 12 days ago

rezock58/best_vit_lpg_phish_v2

reacted to Chris4K's post with 🔥 12 days ago

Can a network whose trunk is only XOR, table lookups and integer adds learn Connect Four? Partly. I trained a GF(2⁸) integer model (8.4M params, CPU-only) on 320k games labelled by a negamax search teacher. What it learned: • agrees with the teacher on 59.6% of training / 41.0% of board-disjoint holdout positions • beats a random player 85.2% of the time What it didn't: • loses to a 1-ply "win now / block now" rule (wins 37.2%) • loses to 2-ply lookahead (wins 24.3%) Agreeing with a teacher is not the same as playing well, and this release measures both. Caveat up front: the float32 read-out is 99.6% of the parameters. The trunk is float-free; the full model isn't yet. The repo also documents six bugs that each produced a plausible but wrong number. In one, 41% of the supervision was silently all-zero. 🎮 Play: https://huggingface.co/spaces/Chris4K/connect-four-gf256 (Space) 🧠 Model: https://huggingface.co/Chris4K/connect-four-gf256 📦 Dataset: https://huggingface.co/Chris4K/connect-four-gf256 320k games + per-ply targets, MIT 💻 Code + paper: https://github.com/Ckal/connect-four-gf256 Feedback welcome, especially: which baseline would convince you?

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