rrm workspace: TRM/HRM/SRM code, Maze dataset, dynamical-analysis pipelineHEADmain

Curated export for clone-and-run Maze training (2x A6000) + diagnostics.
trm/hrm pretrain.py carry trajectory-augmentation code (backward-compatible).
Heavy artifacts (checkpoints/wandb/npz) gitignored; see PROVENANCE.md.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

1 files changed, 84 insertions, 0 deletions

diff --git a/research/flossing/launch_interfloss_queue.sh b/research/flossing/launch_interfloss_queue.sh
new file mode 100755
index 0000000..1c5807f
--- /dev/null
+++ b/research/flossing/launch_interfloss_queue.sh
@@ -0,0 +1,84 @@
+#!/usr/bin/env bash
+set -eo pipefail
+
+FLOSS_DIR="/home/yurenh2/rrm/research/flossing"
+CONDA_SH="/home/yurenh2/miniconda3/etc/profile.d/conda.sh"
+HRM_ROOT="/home/yurenh2/rrm/hrm/checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HierarchicalReasoningModel_ACTV1 righteous-python"
+TRM_ROOT="/home/yurenh2/rrm/trm/checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku_singleGPU"
+
+wait_for_pid() {
+  local pid="$1"
+  if [[ "${pid}" == "0" ]]; then
+    return 0
+  fi
+  while kill -0 "${pid}" 2>/dev/null; do
+    sleep 60
+  done
+}
+
+activate_env() {
+  source "${CONDA_SH}"
+  conda activate rrm
+  cd "${FLOSS_DIR}"
+}
+
+run_hrm_engelken() {
+  wait_for_pid "${1:-0}"
+  activate_env
+  CUDA_VISIBLE_DEVICES=0 python step7_interfloss.py \
+    --model hrm \
+    --ckpt-root "${HRM_ROOT}" \
+    --ckpt-name step_26040 \
+    --train-steps 10000 \
+    --batch-size 8 \
+    --train-lr 1e-5 \
+    --floss-lr 1e-4 \
+    --floss-steps 500 \
+    --interfloss-at 0,500 \
+    --floss-mode engelken_l2 \
+    --lambda-star 0 \
+    --k-lyap 8 \
+    --lyap-act-steps 4 \
+    --seed 42 \
+    --eval-every 1000 \
+    --eval-n 512 \
+    --eval-batch-size 32 \
+    --floss-log-every 10 \
+    --out step7_A_hrm_engelken_interfloss_26040_k8_10k.json \
+    > step7_A_hrm_engelken_interfloss_26040_k8_10k.log 2>&1
+}
+
+run_trm_engelken() {
+  wait_for_pid "${1:-0}"
+  activate_env
+  CUDA_VISIBLE_DEVICES=2 python step7_interfloss.py \
+    --model trm \
+    --ckpt-root "${TRM_ROOT}" \
+    --ckpt-name step_26041 \
+    --train-steps 10000 \
+    --batch-size 4 \
+    --train-lr 1e-5 \
+    --floss-lr 1e-4 \
+    --floss-steps 500 \
+    --interfloss-at 0,500 \
+    --floss-mode engelken_l2 \
+    --lambda-star 0 \
+    --k-lyap 4 \
+    --lyap-act-steps 4 \
+    --seed 42 \
+    --eval-every 1000 \
+    --eval-n 512 \
+    --eval-batch-size 32 \
+    --floss-log-every 10 \
+    --out step7_B_trm_engelken_interfloss_26041_k4_batch4_10k.json \
+    > step7_B_trm_engelken_interfloss_26041_k4_batch4_10k.log 2>&1
+}
+
+cmd="${1:?usage: launch_interfloss_queue.sh MODE [wait_pid]}"
+wait_pid="${2:-0}"
+
+case "${cmd}" in
+  hrm_engelken) run_hrm_engelken "${wait_pid}" ;;
+  trm_engelken) run_trm_engelken "${wait_pid}" ;;
+  *) echo "unknown command: ${cmd}" >&2; exit 2 ;;
+esac