code/configs/control_no_infonce.json

{
  "_doc": "Control A: identical to pilot config BUT lambda_id=0 (no InfoNCE identifiability loss). Tests whether InfoNCE is the load-bearing piece that makes z informative. Hypothesis: without InfoNCE, z collapses to decorative (like Abstract-CoT). 3000 K=4 steps, ~1.6h on GH200. Compare resulting Delta_random/Delta_zero to pilot's step-2000 K=4 result (Delta=3pp at same compute scale) and step-6000 K=8 result (Delta=11pp peak).",

  "base_model": "Qwen/Qwen2.5-1.5B-Instruct",
  "use_lora": true,
  "lora_r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "lora_target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"],
  "dtype": "bfloat16",
  "attn_impl": "eager",

  "K_latents": 4,
  "K_curriculum": [[0, 4]],
  "block_y_to_x": true,
  "proj_init_scale": 0.02,

  "lambda_lm": 1.0,
  "lambda_id": 0.0,
  "lambda_kl": 0.0001,
  "tau_infonce": 0.2,
  "infonce_target": "final_number",

  "lr_lora": 3e-4,
  "lr_proj": 1e-4,
  "lr_head": 3e-4,
  "weight_decay": 0.01,
  "max_grad_norm": 1.0,
  "warmup_steps": 100,

  "batch_size": 16,
  "grad_accum": 2,
  "max_steps": 3000,
  "max_prompt_len": 192,
  "max_answer_len": 192,

  "log_every": 25,
  "eval_every": 500,
  "eval_size": 200,
  "save_every": 3000,
  "seed": 42,

  "output_dir": "/home/ubuntu/work/blt_control_no_infonce",
  "data_train_size": null,
  "data_eval_size": 200
}