r3B_arcmix_qa2x/README.md

r3B_arcmix_qa2x — arm B of round 3: ARC-MIX with Q&A documents upweighted

Why it exists: tests whether upweighting question-answering data helps. ARC-MIX sampled uniformly across the whole corpus (2.62B-token pool), with documents in chat/Q&A format (those containing <|im_start|>) taken twice, raising their share from 3.7% to 7.1%. No educational data. Trained in parallel with arm A (r3A_arcmix_edu_clean/) from the same checkpoint.

Results

checkpoint	step (read from file)	ARC-Easy	BLiMP	WikiText-2 byte-ppl	eff (board formula, 62.9M)
ckpt_340k.pt	340000	46.42	75.71	2.3781	75.23
ckpt_360k.pt	360000	47.73	75.77	2.3744	75.71
ckpt_380k.pt	380000	47.39	75.77	2.3756	75.59
ckpt_400k.pt	400000	47.26	75.90	2.3712	75.60

Verdict: see r3A_arcmix_edu_clean/ — A − B shows no difference on eff (400k: +0.11 [−0.30, +0.51]). A follow-up round compares this arm against an anchor with the same pool size and no upweighting (r4K_anchor_arcmix_qa1/) and against the same data with a different seed (r4B_arcmix_qa2x_seed1338/); first readings suggest the 2× Q&A upweight slightly hurts rather than helps, not yet beyond noise.

Common setup

• Base model: GoLLeM-v5 64M flagship (v1_muon/, 62.9M parameters, 14 layers, d_model 576, 9 heads, RoPE, SwiGLU, RMSNorm, QK-norm, value residual, Muon optimizer). Every arm of the study starts from the flagship checkpoint at step 320,000 and continues to step 400,000 (80k steps, about 2.6B tokens) with the flagship recipe unchanged (same learning-rate schedule, batch, optimizer state and seed). Only the training data differs.
• Method: two arms trained in parallel from the same checkpoint, evaluated at matching steps (360k / 400k for the first round, 340k to 400k for round 3) with the same harness; the difference between arms is attributed to the data.
• Evaluation: glint_parity_eval.py in the repository root (fixed version: BLiMP on exactly 67,000 pairs), ARC-Easy test (bare prompt), BLiMP, WikiText-2 test byte-perplexity, eff by the Glint board formula. The step in the tables is read from the checkpoint file, not from its name.
• Reference: flagship v1_muon/ckpt_400k.pt scores ARC-Easy 47.94 / BLiMP 75.83 / byte-ppl 2.3718 / eff 75.81. For scale: a second run that differs only in the random seed moved eff by about 0.26 (one seed pair, so a rough indication of run-to-run noise, not a precise estimate).
• Status: research checkpoint, not a leaderboard submission. No arm of this study beats the flagship beyond run-to-run noise.
• Format: PyTorch checkpoint dict with model, opt, step, config; train_gpt_ref.py in the repository root rebuilds the model from config.