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transformerless_lm: Pareto bench results — composed wins, K-extension/scale lose
Full 1500-step bench at TinyShakespeare with lazy-loading. Three blocks each tested a follow-up direction: config params val vs dense compression dense_crt_d128 801,664 2.5778 - 1.0x fibgen_K32_cross_d128 80,640 2.7731 +7.6% 10.0x * fibgen_K48_cross_d128 172,800 3.3806 +31.1% 4.7x fibgen_K64_cross_d128 301,824 3.1486 +22.1% 2.7x dense_crt_d256 3,176,192 2.6098 - 1.0x fibgen_K32_cross_d256 87,552 3.3878 +29.8% 36.5x transformerless_K32_cross 213,752 2.7211 +5.6% 3.8x ** * Pareto sweet spot for plain FibGen ** WINNER -- composed substrate primitives VERDICTS: TOSS Block 1 (K-extension): K=32 is the Pareto sweet spot for plain FibGen. K=48 and K=64 LOST at d=128 (+31% and +22% gap vs K=32's +7.6%). Higher Fibonacci frequencies wrap pseudo-randomly mod d and act as interference, not basis. "More K = more capacity" is falsified. TOSS Block 2 (scale to d=256): FibGen DOES NOT scale to bigger dense models. Compression rises nicely (10x -> 36x) but loss penalty rises with it (+7.6% -> +29.8%). The K=32 basis isn't rich enough for the capacity dense models use at higher d_model. For the substrate to scale to LLM dimensions, K would need to grow roughly as sqrt(d_model). Direct path to "35B in 8GB" via plain FibGen alone is BLOCKED at this scale. KEEP Block 3 (composed transformerless): The full substrate stack WINS: - CRT-Fibonacci positional encoding - FibGen embedding - Fibonacci-offset sparse attention (only F(k)-distance pairs) - FibGen QKV/out weights inside the sparse attention - Zeckendorf-routed FFN (5 specialists, routed by token-id Zeckendorf) - FibGen specialist weights - FibGen LM head Composed val 2.7211 BEATS plain FibGen K=32's 2.7731 -- the Fibonacci-offset attention + Zeckendorf-routed FFN do NOT interfere with FibGen weights; they make it BETTER. Compression is 3.8x (less than plain FibGen's 10x) but the loss/compression Pareto moves up and to the left -- this is the best operating point we have found. NEXT QUESTIONS to investigate: - Why does FibGen lose at d=256? Likely needs K to scale with d. Test FibGen K=64 at d=256 (matching K^2/d^2 ratio). - Can the composed transformerless arch beat dense outright at longer training? At step 1500 the gap is only +5.6%. - Does the composed arch hold its win at d=256?
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‎experiments/transformerless_lm/results_fibgen_pareto.json‎

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