feat: r64_overnight sweep — focused rank-64 ablation at 8000 steps

15 experiments across rank (64/128), alpha, regularisation, LR, target
layers, and combined stacks. Based on tier1_thorough early results
confirming rank 64 sounds best perceptually.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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{
"name": "r64_overnight",
"description": "Focused rank-64 overnight sweep. All experiments use rank 64 as base — confirmed best from tier1_thorough early results. 8000 steps to reach convergence (none converged at 4000).",
"data_dir": "/media/unraid/davinci/Selva/BJ/features",
"output_root": "/media/unraid/davinci/Selva/BJ/experiment/r64_overnight",
"base": {
"steps": 8000,
"rank": 64,
"alpha": 0.0,
"lr": 1e-4,
"batch_size": 16,
"warmup_steps": 200,
"grad_accum": 1,
"save_every": 2000,
"seed": 42,
"target": "attn.qkv",
"timestep_mode": "uniform",
"logit_normal_sigma": 1.0,
"curriculum_switch": 0.6,
"lora_dropout": 0.0,
"lora_plus_ratio": 1.0
},
"experiments": [
{
"id": "g1_r64_baseline",
"group": "rank",
"description": "Rank 64 baseline — clean reference at 8000 steps."
},
{
"id": "g1_r128_baseline",
"group": "rank",
"description": "Rank 128 — 102GB VRAM makes this free. Does doubling rank from 64 help further?",
"rank": 128
},
{
"id": "g2_r64_alpha_32",
"group": "alpha",
"description": "Rank 64 alpha=32 (scale=0.5). Reduces intruder singular dimensions (arXiv:2410.21228).",
"alpha": 32.0
},
{
"id": "g2_r64_alpha_16",
"group": "alpha",
"description": "Rank 64 alpha=16 (scale=0.25). More aggressive scale reduction — may over-constrain.",
"alpha": 16.0
},
{
"id": "g3_r64_lora_plus",
"group": "regularisation",
"description": "LoRA+ ratio=16. lr_B = 16 × lr_A. Faster convergence at constant step budget.",
"lora_plus_ratio": 16.0
},
{
"id": "g3_r64_dropout_0.05",
"group": "regularisation",
"description": "Dropout=0.05. Light sparsity regularisation on LoRA path.",
"lora_dropout": 0.05
},
{
"id": "g3_r64_dropout_0.1",
"group": "regularisation",
"description": "Dropout=0.1. Stronger regularisation — tests if 49 clips needs heavier constraint.",
"lora_dropout": 0.1
},
{
"id": "g3_r64_curriculum",
"group": "regularisation",
"description": "Curriculum sampling: logit_normal for steps 1-4800, then uniform (arXiv:2603.12517).",
"timestep_mode": "curriculum"
},
{
"id": "g4_r64_lr_low",
"group": "lr",
"description": "LR=3e-5. 3× lower — checks if 1e-4 is overshooting at rank 64.",
"lr": 3e-5
},
{
"id": "g4_r64_lr_high",
"group": "lr",
"description": "LR=3e-4. 3× higher — may converge faster but risk instability.",
"lr": 3e-4
},
{
"id": "g5_r64_target_full",
"group": "target",
"description": "Rank 64 targeting attn.qkv + linear1 (FFN projections). Doubles LoRA coverage.",
"target": "attn.qkv linear1"
},
{
"id": "g5_r128_target_full",
"group": "target",
"description": "Rank 128 + full target. Maximum possible coverage with available VRAM.",
"rank": 128,
"target": "attn.qkv linear1"
},
{
"id": "g6_r64_full_tier1",
"group": "combined",
"description": "All Tier 1 at rank 64: LoRA+ 16 + dropout 0.05 + curriculum. Full stack at 8000 steps.",
"lora_plus_ratio": 16.0,
"lora_dropout": 0.05,
"timestep_mode": "curriculum"
},
{
"id": "g6_r64_alpha32_full",
"group": "combined",
"description": "Rank 64 alpha=32 + all Tier 1. Best alpha scaling + best regularisation stack.",
"alpha": 32.0,
"lora_plus_ratio": 16.0,
"lora_dropout": 0.05,
"timestep_mode": "curriculum"
},
{
"id": "g6_r128_full_tier1",
"group": "combined",
"description": "Rank 128 + all Tier 1. Tests if more capacity + regularisation beats rank 64 full.",
"rank": 128,
"lora_plus_ratio": 16.0,
"lora_dropout": 0.05,
"timestep_mode": "curriculum"
}
]
}