CoolFace
Modelpublic

ceselder/loracle-ablation-N2500-loras

sourceHugging Faceupdated 5mo agoView on Hugging Face
0likes
Model Card

# loracle-ablation-N2500-loras

Scaling-law ablation point: trained on N=2500 unique LoRAs (vs the 25k baseline). Part of a 5-point sweep where the only thing varying is the number of unique training LoRAs; everything else (interpreter rank=256, alpha=32, lr=3e-5, accum=8, warmup=10% of opt-steps, 1 epoch, AO encoder, rslora=true) held fixed.

## Eval at end of epoch (final step)

Judge: Sonnet 4.6 via OpenRouter, canonical IA-paper rubric.

Setorganismsany-matchrollout-mean
heldout_ia2045%19.2%
triggerrecoveryheldout_ia2040%15.0%
auditbench5614.3%4.8%
oodmodelsv32718.5%10.5%
val/mean_all_evals—29.5%—
average rollout-mean across 4 sets——12.36%

train/global_step: 625 val/loss: 2.086

Note: any-match is "did ≥1 prompt × rollout hit per organism"; with 20-org sets it has high variance (each match shifts the rate by 5pp). rollout-mean averages over all (org × prompt × rollout) rows so it's a less noisy summary statistic, especially for the small eval sets.

## Full 5-point ablation curve

Nstepval_lossmean (any)hia (any/rm)trg (any/rm)ab (any/rm)ood (any/rm)avg rm
2,5006252.08629.5%45/19.240/15.014.3/4.818.5/10.512.36%←
5,00012501.88127.1%35/15.815/8.825.0/8.333.3/12.711.41%
7,50018751.92827.3%40/15.820/8.819.6/7.129.6/14.111.45%
10,00025001.89029.5%45/18.320/6.223.2/8.929.6/15.612.28%
25,00031251.94531.5%40/21.715/6.230.4/9.840.7/16.813.64%

Trends:

  • —avg rollout-mean climbs ~modestly with N: 12.36% → 13.64% from N=2500 to N=25000
  • —auditbench rollout-mean: 4.8 → 8.3 → 7.1 → 8.9 → 9.8 (cleanly monotonic)
  • —ood_models_v3 rollout-mean: 10.5 → 12.7 → 14.1 → 15.6 → 16.8 (cleanly monotonic)
  • —Small-eval-set noise dominates heldoutia and triggerrecovery any-match scores

## Wandb

This run: https://wandb.ai/adamkarvonen/lora-oracles/runs/8om8yw0j Tag: ablation, scaling-law-loras, N2500

## Layout

  • —interpreter/ — PEFT LoRA adapter
  • —encoder.pt — AO encoder state_dict
  • —ao.pt — AO norm-match hook params
  • —tokenizer/ — Qwen3-14B tokenizer
  • —loracle_config.yaml — training config snapshot