_runs/launch_adaptive_latent_baseline.sh

#!/usr/bin/env bash
# Adaptive-k thought-token baseline (experiment D in the 2x2 ablation).
#
# Same single-stage, whole-puzzle setup as launch_simple_baseline.sh
# (experiment C, the "strawman"); same model, LoRA, JSONL, chat template.
# The ONLY change is that the SFT loss uses the recurrent_hidden mechanism
# with k thought tokens, and k grows automatically when the rolling-mean
# loss plateaus.
set -euo pipefail

ROOT=/home/ubuntu/curriculum_cot
SCRIPT=${ROOT}/_runs/adaptive_latent_baseline_sudoku_train.py
PYTHON_BIN=/opt/pytorch/bin/python

TRAIN_JSONL=${ROOT}/data/sudoku_t3_20empty_value_qwen_text_stage1_train.jsonl
EVAL_JSONL=${ROOT}/data/sudoku_t3_20empty_value_qwen_text_stage1_eval.jsonl

SWEEP_ROOT=${ROOT}/_runs/adaptive_latent_$(date +%Y%m%d_%H%M%S)
mkdir -p "${SWEEP_ROOT}"
echo "${SWEEP_ROOT}" > "${ROOT}/_runs/current_adaptive_latent_sweep_dir"
echo "SWEEP_ROOT=${SWEEP_ROOT}"

export TOKENIZERS_PARALLELISM=false
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
export HF_HOME="${ROOT}/.hf_cache"
export TRANSFORMERS_CACHE="${ROOT}/.hf_cache"
export WANDB_MODE=offline

run_variant() {
  local gpu="$1" tag="$2" lr="$3" max_k="$4" min_steps_per_k="$5"
  local out=${SWEEP_ROOT}/${tag}
  mkdir -p "${out}"
  local log=${out}/train.log
  : > "${log}"
  (
    export CUDA_VISIBLE_DEVICES="${gpu}"
    "${PYTHON_BIN}" -u "${SCRIPT}" \
      --train_jsonl "${TRAIN_JSONL}" \
      --eval_jsonl "${EVAL_JSONL}" \
      --output_dir "${out}" \
      --learning_rate "${lr}" \
      --max_steps 4000 \
      --per_device_train_batch_size 4 \
      --gradient_accumulation_steps 2 \
      --logging_steps 25 \
      --save_steps 500 \
      --eval_every_steps 500 \
      --eval_rows 50 \
      --max_completion_length 96 \
      --max_prompt_length 1024 \
      --lora_r 32 --lora_alpha 64 --lora_dropout 0.05 \
      --enable_gradient_checkpointing \
      --start_k 0 \
      --max_k "${max_k}" \
      --min_steps_per_k "${min_steps_per_k}" \
      --plateau_window 100 \
      --plateau_eps 0.005 \
      --converged_eps 0.001 \
      --seed 0 \
      >> "${log}" 2>&1
  ) >/dev/null 2>&1 &
  local pid=$!
  echo "$pid $gpu $tag" >> "${SWEEP_ROOT}/PIDS.txt"
  disown $pid 2>/dev/null || true
  printf 'GPU %s -> %s pid=%s log=%s\n' "$gpu" "$tag" "$pid" "$log"
}

# 2 variants on idle GPUs 2,3:
#  - adaptive_a:  same LR (5e-5) as strawman variant a, max_k=4, min_steps_per_k=400
#  - adaptive_b:  smaller min_steps_per_k=250 to grow k more aggressively
run_variant 2 adaptive_a_lr5e5_maxk4    5e-5  4  400
run_variant 3 adaptive_b_lr5e5_fastgrow 5e-5  4  250

echo
echo "=== launched ==="
cat "${SWEEP_ROOT}/PIDS.txt"