#!/bin/bash
# Wait for HRM256 baseline to finish, then run SRM v1 scaled to 7M (TRM-equiv size).
set -e
cd /home/yurenh2/rrm/srm
source /home/yurenh2/miniconda3/etc/profile.d/conda.sh
conda activate rrm
export CUDA_VISIBLE_DEVICES=2

HRM256_RUNNER_PID=$1
LOG_DIR=/home/yurenh2/rrm/research/flossing
echo "[$(date '+%H:%M:%S')] SRM7M-runner waiting for HRM256-runner PID $HRM256_RUNNER_PID..." >> $LOG_DIR/step4_runner.log
while kill -0 "$HRM256_RUNNER_PID" 2>/dev/null; do
  sleep 30
done
echo "[$(date '+%H:%M:%S')] HRM256 done. Starting SRM v1 scaled to 7M (n_aol_layers=5, hidden=512)" >> $LOG_DIR/step4_runner.log

mkdir -p runs ckpts

# SRM v1 at TRM-equivalent param count (~7M)
python scripts/train_srm.py \
  --n-steps 3000 --batch-size 8 \
  --hidden-size 512 --n-iters 12 --n-aol-layers 5 \
  --kappa 0.9 --eta 1.0 --alpha 1.0 \
  --warmup-steps 200 \
  --k-lyap 2 --lyap-iters 8 --lyap-every 50 \
  --eval-every 300 --eval-n 512 --eval-batch-size 32 \
  --out runs/srm_v1_7M_sudoku1k_3k.json \
  --save-ckpt ckpts/srm_v1_7M_3k.pt \
  > $LOG_DIR/srm_v1_7M_run.log 2>&1

echo "[$(date '+%H:%M:%S')] SRM v1 7M training complete" >> $LOG_DIR/step4_runner.log