LeakyReLU(0.75)² + Legal TTT + Parallel Muon — 1.1185 BPB (3-seed mean)#977
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LeakyReLU(0.75)² + Legal TTT + Parallel Muon — 1.1185 BPB (3-seed mean)#977michaelwinczuk wants to merge 1 commit intoopenai:mainfrom
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One-line activation change (negative_slope 0.5→0.75) + minor LR/warmdown tuning. Discovered via multi-agent think tank swarm research system. 3-seed results with legal TTT: Seed 1337: 1.1183 BPB (15.96MB) Seed 42: 1.1194 BPB (15.96MB) Seed 2024: 1.1179 BPB (15.95MB) Mean: 1.1185 BPB
manfromnowhere143
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Negative slope 0.9 preserves more gradient flow for negative inputs. Combined with EVAL_STRIDE=32 + TTT tuning, targeting 1.1144 BPB. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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slope 0.75 + LR 0.027 + warmdown 3700 (PR openai#977) No SWA with QAT (PR openai#989) QAT from 50% + range fix [-31,31] mHC 22-param residual mixing (PR openai#928) VE128 + no gated_attn + no value_residual (PR openai#549) LZMA preset 7 compression (PR openai#999) Muon TTT with NS3 (PR openai#999) Entropy-adaptive TTT epochs 2/3/4 (PR openai#999) Per-layer TTT LR (PR openai#995) TTT momentum 0.95 (PR openai#995)
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Summary
negative_slope=0.5→0.75MATRIX_LR=0.027,WARMDOWN_ITERS=37003-Seed Results
Key Finding
Systematic sweep of LeakyReLU
negative_slopefound that 0.75 beats the SOTA default of 0.5 by ~0.008 BPB. Higher slope passes 2.25× more gradient through negative pre-activations, accelerating convergence in a capacity-constrained 600-second training window.This was discovered by a multi-agent think tank swarm — 8 specialist AI agents traversing competition-specific knowledge graphs, with validation from Grok, Claude Opus, and Gemini. Full details in the README.
Changes from SOTA (PR #549)
negative_slope=0.75(was 0.5)MATRIX_LR=0.027(was 0.025)WARMDOWN_ITERS=3700(was 3500)All other architecture and hyperparameters identical.
Hardware
8×H100 SXM (RunPod), PyTorch 2.7.1 + CUDA 12.6 + flash-attn v3.