MachineSex/results/llm_merge_hpc

llm_merge_hpc — recombining specialist LLMs at scale (7B, Imperial CX3; blueprint C2/C4)

Claim tested. The scale-up of llm_merge: at a capable base, does recombining decorrelated specialists produce a model that exceeds every single parent overall and stays competent across all families (the Fisher–Muller "offspring fitter than any parent" signature, E8) — the claim that was only marginal at 0.5 B? Run on one L40S (46 GB) GPU node of Imperial College's CX3 HPC (job walltime 8 min).

Setup. Base model Qwen2.5-7B-Instruct (Apache-2.0). Same three disjoint, procedurally- generated task families with an exact-match verifier (the "reality that says no"): lists, strings, arith, deliberately hard so specialists decorrelate. One LoRA specialist (r=16, α=32, 3 epochs, 800 train tasks) is fine-tuned per family, then the base, each specialist, and two weight-space merges — soup (averaged LoRA deltas) and ties (sign-reconciled union) — are evaluated on a held-out mixed test set. Seed 1, 200 test tasks/family.

Results (accuracy)

model	lists	strings	arith	overall	worst family
base	0.46	0.69	1.00	0.71	0.46
spec: lists	0.57	0.74	1.00	0.77	0.57
spec: strings	0.32	0.97	1.00	0.76	0.32
spec: arith	0.47	0.80	0.96	0.74	0.47
merge: soup	0.62	1.00	1.00	0.87	0.62
merge: ties	0.62	1.00	0.99	0.87	0.62

What holds (the 0.5 B caveats resolved)

• "Exceeds every parent overall" — now clean. Both merges reach 0.87 overall, above the best single specialist (lists, 0.77) by a decisive 10 points, and above every specialist on every family (lists 0.62 > 0.57; strings 1.00 > 0.97; arith ≈ 1.00). This is the strict Fisher–Muller claim — offspring fitter than any parent — which was only marginal at 0.5 B and is now clean.
• "Retains all specialties" — sharper than ever. The merges are the only models competent across all families: worst-family 0.62, versus ≤ 0.57 for every specialist (strings-specialist collapses to 0.32 on lists). The generalist assembled from specialists dominates on both axes.
• The dilution is gone. At 0.5 B, averaging diluted the lists-specialist (0.43 → 0.26). At 7 B the merge exceeds the lists-specialist on lists (0.62 > 0.57). A capable base has enough headroom that weight-space averaging composes rather than dilutes — the "merge, don't average" concern (E4) softens once the parents are strong. Soup and ties are indistinguishable at K=3 here.

Takeaway

The scale-up firms up the sign the prototype left marginal. At a capable base, recombining decorrelated specialists yields a model that beats every parent both overall and per-family, with no dilution — the sexual-reproduction / Fisher–Muller claim reproduced cleanly in real LLM weights. The 0.5 B llm_merge prototype de-risked the pipeline and flagged dilution as the risk; the 7B run shows that risk is a small-model artefact. Falsifier (not triggered): a single specialist matching or beating the merge overall — here the merge leads by 10 points. Provenance: L40S, torch 2.12.1 / transformers 5.13.0 / peft 0.19.1, manifest.json records the results hash and library versions.

Note: this run's manifest.json has git_commit: null because it was produced on the HPC node from an rsync'd (non-git) working copy; the committed artefacts here are the source of truth for the figure.