--- license: apache-2.0 pretty_name: "Extrinsic evaluations: the union view across the mergeability workstreams" tags: - model-merging - mergeability - evaluation - benchmarks - likelihood size_categories: - 1K One inconsistency **in a source** is worth flagging: `compose-audit`'s headline §4 quotes the > pythia-14m aligned merge at BLiMP 0.544, while its own authoritative table reports 0.533. This > file carries **0.533**, the table value. It does not change the direction of the finding. --- ## What this data supports, and what it does not ### Supported **The likelihood rescue does not transfer to accuracy.** On PolyPythia seed pairs — same data, same architecture, same tokenizer, so the merge obstruction is purely coordinate — permutation alignment removes ~70% of the naive merge's Δfloor at 14m, and the merged model still scores 0.533 on BLiMP against parents at 0.652 and chance 0.500. Across the ladder the merged model sits between 0.518 and 0.543 at **every** size, whether alignment recovered three quarters of the likelihood gap or a tenth of it. Pair by pair the two rescues are uncorrelated (Spearman 0.14 / −0.17 / 0.17 / 0.05 / 0.23). Both arms are in this file, on the same merges, in separate `metric_kind` groups — which is the whole reason the file is shaped this way. **The dissociation runs in both directions.** Goldfish merges whose Δfloor says they are destroyed still score 0.68 on MultiBLiMP-English (parent 0.96, chance 0.50). Neither metric implies the other. **Alignment's coordinate rescue decays with scale.** The exactly function-preserving permutation rung removes **70% of the naive Δfloor at 14m and 8% at 410m**. The coordinate-removable share of the obstruction is falling in the direction the field is scaling. (The source reports this as a mean of per-pair percentages; recomputing it as a ratio of means from these rows gives 70% → 9%. Same conclusion, slightly different estimator — read the source's tables for the canonical figure.) **Naive averaging of two seed-only-different LMs is catastrophic at every size**, and alignment does not make it usable: even the best rung leaves the merge at or above the uniform-over-vocabulary reference at the small sizes. **Joint training beats every merge tested.** The B-GPT `jointly_trained` rows are the ceiling, and no merge in this file reaches them on either metric. **AIM's published benefit is real.** Paired across 20 matched checkpoints, the with-AIM arm is up on the endpoint-scaled benchmark mean (18/20 positive, Wilcoxon p = 9.5e-06). Those published outcomes are here for both arms. ### The headline nulls — read these before quoting anything positive **0 of 1035 MergeBench cells survive Benjamini-Hochberg at n = 8 families (80 pairs).** No property-vs-outcome relationship in the MergeBench panel is distinguishable from zero at the coverage its published outcomes permit. An earlier draft reported 306 surviving cells; all were artefacts of three defects since fixed — a cluster bootstrap with a 40.2% false-positive rate at n = 4, a saturation gate calibrated on pair-level rather than family-level spread, and testing against zero when the families cluster by architecture. **This dataset carries only MergeBench's published outcome scores, not those correlation cells** — the cells live in `Mergeability-2/mergebench-property-sweep`, and `table_domain_cells.csv` is what to read. **0 of 13 released Llama-3.1-8B derivatives have left the base parameterisation.** Across every released derivative examined — language forks, domain continued pretraining, instruct post-training, a safety model — not one had moved out of the base model's coordinate frame. The aligned and naive chat vectors are therefore **bit-identical models**, and the accuracy difference in this file is exactly `0.000` on every benchmark, for every fork, at every λ. That is a null about the **ecosystem**, not about the mechanism: the `control` rows, where a real fork is acted on by a random element of its own symmetry group, show the naive chat vector collapsing (IFEval 0.175 → 0.110, below the fork it started from) and alignment restoring it to 0.355. The mechanism reaches accuracy. The condition that would make it pay off did not occur in any released model. **Pre-merge predictors do not reliably predict the realised rescue** — 0 of 25 cells significant, held out by seed pair. The strongest predictor does not replicate across substrates (held-out AUROC 0.48 / 0.71 / 0.81 / 0.61 / 0.45). **AIM does not change what makes a merge work.** Not one of 216 property-vs-outcome correlation cells survives multiplicity correction, and the with-minus-without change is inside the noise band from re-splitting the same benchmarks within one arm. ### Not supported — do not use this file for these - **Pair-level MergeBench merge outcomes.** They do not exist. Every score MergeBench publishes is a **five-expert** merge (`merge_arity` = 5 in all 576 rows), so the 80-pair property suite has no published counterpart. `parents` says so on every MergeBench row. - **Cross-source comparisons of raw `value`.** Substrates, corpora, item budgets and scorers differ between experiments. Compare within an `experiment`, or within a (`experiment`, `benchmark`, `metric`) group. - **Anything about merged-model accuracy at 1B+ scale.** The 1B arm in this file is likelihood-only; the accuracy arm (`Mergeability-2/crossarch-accuracy`) was not published at build time. The checkpoint-distance rule — keep souped checkpoints within about half a decade of training steps — is measured in **nats/token, not benchmark accuracy**, and given the dissociation above it must not be assumed to transfer. - **Complete Beetle coverage.** 492 rows are in, but the source was mid-run: of the merges it tracks, several are still `not evaluated`. Read `coverage_by_merge.csv` in the source before quoting a Beetle aggregate. - **A practitioner diagnostic.** The scientific claim (merge failure can arise from how a function is represented, and that is measurable) is well supported. The practitioner claim (compute this before merging and it tells you what to do) is **not supported** at the coverage available. - **Significance testing.** This file carries measurements, not tests. Every p-value, bootstrap and permutation null lives in the source datasets. --- ## Rebuilding ```bash source /root/.ms_hf_env # HF_TOKEN python3 build_extrinsic.py # rebuild from the Hub python3 build_extrinsic.py --push # rebuild and publish ``` Idempotent: it re-fetches each source, skips any that 404, rebuilds every row from scratch, re-runs the dedupe, and refuses to emit an accuracy row without a chance level. Re-run it when `crossarch-accuracy` publishes, and again once `beetle-merge-eval` finishes its run. --- ## Row counts | experiment | accuracy | accuracy_delta | benchmark_score | likelihood | likelihood_delta | other | total | |---|--:|--:|--:|--:|--:|--:|--:| | polypythia_seed_merge | 1095 | 477 | 0 | 1413 | 1689 | 0 | **4674** | | polypythia_corpus_robustness | 0 | 0 | 0 | 486 | 432 | 0 | **918** | | crossarch_checkpoint_merge | 0 | 0 | 0 | 657 | 106 | 0 | **763** | | goldfish_crosslingual | 0 | 0 | 0 | 609 | 0 | 0 | **609** | | mergebench_published_outcomes | 0 | 0 | 576 | 0 | 0 | 0 | **576** | | beetle_merge_eval | 468 | 0 | 0 | 0 | 0 | 24 | **492** | | crossarch_transport_merge | 0 | 0 | 0 | 320 | 0 | 0 | **320** | | goldfish_bilingual_merge | 68 | 56 | 0 | 84 | 108 | 0 | **316** | | aim_published_outcomes | 264 | 0 | 0 | 0 | 0 | 40 | **304** | | bgpt_bilingual_merge | 56 | 0 | 0 | 56 | 60 | 0 | **172** | | chat_vector_llama31 | 144 | 0 | 0 | 0 | 0 | 0 | **144** | | goldfish_bilingual_merge_reverse | 0 | 0 | 0 | 48 | 72 | 0 | **120** | | crossgroup_direct_merge | 80 | 0 | 10 | 0 | 0 | 0 | **90** | | bgpt_joint_vs_merge | 40 | 0 | 0 | 40 | 0 | 0 | **80** | | polypythia_ablation | 0 | 0 | 0 | 36 | 42 | 0 | **78** | | crossarch_native_merge | 0 | 0 | 0 | 10 | 4 | 0 | **14** | | crossarch_1b_native | 0 | 0 | 0 | 5 | 0 | 0 | **5** | | **All** | **2215** | **533** | **586** | **3764** | **2513** | **64** | **9675** | 3,472 distinct models. By role: 8,698 merged · 323 parent · 248 jointly_trained · 210 control · 196 reference. ## Example ```python import pandas as pd df = pd.read_parquet("extrinsic_evaluations.parquet") # the dissociation, in two lines seed = df[df.experiment == "polypythia_seed_merge"] acc = seed[(seed.metric == "accuracy") & (seed.model_role == "merged")] nats = seed[(seed.metric == "delta_floor_nats_per_token") & (seed.model_role == "merged")] print(acc.groupby(["provenance_family", "rung"]).value.mean()) # flat, ~0.52-0.54 print(nats.groupby(["provenance_family", "rung"]).value.mean()) # collapses with alignment # never do this df.groupby("benchmark").value.mean() # mixes nats with accuracy. group by metric_kind. ```