TRAINING.md

Training

Start from the environment setup in the README. Training uses the same Python environment as evaluation, plus third_party/verl-agent/requirements.txt.

1. Prepare SFT data

cd JAMEL

python jamel/train/memory/prepare_sft_dataset.py \
  --input /path/to/trajectory.parquet \
  --output data/jamel_sft_data \
  --compressor-model /path/to/Qwen3-VL-2B-Instruct \
  --max-memory-items 512 \
  --max-length 8192 \
  --val-ratio 0.02 \
  --compression-batch-size 4

This writes:

data/jamel_sft_data/jamel_memory_sft_train.parquet
data/jamel_sft_data/jamel_memory_sft_val.parquet

2. Train and package the model

TRAIN_FILE=data/jamel_sft_data/jamel_memory_sft_train.parquet \
VAL_FILE=data/jamel_sft_data/jamel_memory_sft_val.parquet \
BASE_MODEL_PATH=Qwen/Qwen2.5-VL-7B-Instruct \
COMPRESSOR_MODEL=/path/to/Qwen3-VL-2B-Instruct \
OUTPUT_DIR=outputs/jamel_sft_ckpt \
OUTPUT_MODEL_PATH=outputs/jamel_model \
NPROC_PER_NODE=8 \
TOTAL_EPOCHS=1 \
VAL_STEPS=200 \
bash shell/run_qwen25vl_7b_sft.sh

OUTPUT_DIR stores actor checkpoints such as global_step_*. NPROC_PER_NODE is the number of training GPUs used by torchrun. OUTPUT_MODEL_PATH stores the final JAMEL model used by evaluation:

outputs/jamel_model/
  actor/
  compressor/
  model.json

Use OUTPUT_MODEL_PATH as MODEL_PATH in EVALUATION.md.