Giorgio Gilestro
e433e48860
Adds the "directed sex" operator (E10) the moe regime-flip pointed to: don't
commit to one a-priori blend — breed a population of recombinant offspring
(specialists merged at Dirichlet-sampled weights), score each on a held-out
validation split with the verifier, and keep the fittest, reported on a fresh
test split. Two breeding objectives: best-overall and best-worst-family.
src/llm/directed.py + kind llm_directed, reusing the cached specialists.
Result — refinements pay off in proportion to how far the uniform soup is from
optimal:
- 0.5B (soup dilutes): directed selection beats soup on the bred objective —
directed_overall 0.69 > soup 0.64; directed_balanced worst-family 0.37 > 0.26.
Riders: single-objective selection trades off the other axis (overall-breed
tanks lists to 0.17); a global blend still trails per-input routing (0.74).
- 7B (Imperial CX3, soup already composes to ceiling on near-saturated families,
strings/arith 1.00): directed ~= soup (0.868 ~ 0.873, marginally below via a
val/test overfit gap) — no fitter offspring to breed.
Through-line across all four LLM runs: "merge, don't average" and its refinements
(routing, directed selection) are weak-base / suboptimal-default phenomena — they
help at 0.5B and are inert at 7B. Honest limitation kept in the writeup: the 7B
families are near-saturated, which caps the headroom; a harder unsaturated
benchmark is the fair next test.
Also folds in the two llm_moe local manifest/config files missed in 8da0dac.
+3 directed unit tests (130 green). Results in results/llm_directed{,_hpc}/
(parquet gitignored).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
45 lines
2.2 KiB
Makefile
45 lines
2.2 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
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# reproducibility source of truth; every target runs inside it via `uv run`.
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.PHONY: env env-neural env-mnist env-llm test layer1 layer2 neural mnist llm figures clean
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env: ## build .venv from the committed lockfile
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uv sync --extra dev
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env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
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uv sync --extra dev --extra neural
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env-mnist: ## add torchvision for the real-MNIST confirmation tier
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uv sync --extra dev --extra neural --extra mnist
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test: ## correctness tests + scientific-validation tests (the spine of trust)
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uv run pytest
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layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
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for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the heavy MNIST tier)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the real-MNIST confirmation tier (needs env-mnist; downloads MNIST)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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env-llm: ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
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uv sync --extra dev --extra neural --extra llm
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llm: ## run the LLM prototypes: merge (fusion) + moe (union) + directed (offspring select)
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uv run python -m llm.experiment configs/llm/merge.yaml
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uv run python -m llm.experiment configs/llm/moe.yaml
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uv run python -m llm.experiment configs/llm/directed.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
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for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
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*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
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clean: ## remove caches and generated results (keeps committed manifests)
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rm -rf .pytest_cache **/__pycache__
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find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
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