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, 35 insertions, 0 deletions

diff --git a/scripts/queue_trm_sudoku.sh b/scripts/queue_trm_sudoku.sh
new file mode 100755
index 0000000..fa447ff
--- /dev/null
+++ b/scripts/queue_trm_sudoku.sh
@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+# Queue TRM Sudoku 1k 4-GPU training: wait for Step 3 to finish then launch.
+# Run via: nohup bash scripts/queue_trm_sudoku.sh > runs/trm_queue.log 2>&1 &
+set -e
+LOG_DIR=/home/yurenh2/rrm/runs
+mkdir -p "$LOG_DIR"
+TRM_LOG="$LOG_DIR/trm_sudoku_$(date +%Y%m%d_%H%M%S).log"
+echo "[$(date)] queued, waiting for step3_train processes to exit..."
+
+while pgrep -f step3_train_with_rf >/dev/null; do
+  sleep 60
+done
+
+echo "[$(date)] Step 3 done — launching TRM Sudoku 1k 4-GPU"
+
+source "$(conda info --base)/etc/profile.d/conda.sh"
+conda activate rrm
+
+cd /home/yurenh2/rrm/trm
+
+# Official TRM Sudoku 1k pretrain_mlp_t_sudoku command, scaled to 4-GPU torchrun
+WANDB_MODE=offline OMP_NUM_THREADS=8 \
+  torchrun --standalone --nproc-per-node 4 pretrain.py \
+    arch=trm \
+    data_paths="[/home/yurenh2/rrm/data/sudoku-extreme-1k-aug-1000]" \
+    evaluators="[]" \
+    epochs=50000 eval_interval=5000 \
+    lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0 \
+    arch.mlp_t=True arch.pos_encodings=none \
+    arch.L_layers=2 \
+    arch.H_cycles=3 arch.L_cycles=6 \
+    +run_name=pretrain_mlp_t_sudoku ema=True \
+    > "$TRM_LOG" 2>&1
+
+echo "[$(date)] TRM training finished. log: $TRM_LOG"