LAR-9B

# Install the corresponding environment
# (run from the repository root; needs python3.12 + venv, git, docker with compose v2 (user in the docker group), sudo,
#  an NVIDIA driver and CUDA toolkit for CUDA 13; MODEL = LAR-27B checkpoint, WORK = work dir on a fast local disk)
export MODEL=amazon/LAR-27B WORK=/path/to/local/disk/tb21
python3.12 -m venv ~/.venvs/lar-serve
~/.venvs/lar-serve/bin/pip install -r PSWF/EvaluationKit/TB-2.1/requirements/serve.txt ./PSWF/EvaluationKit/TB-2.1
python3.12 -m venv ~/.venvs/lar-eval
~/.venvs/lar-eval/bin/pip install -r PSWF/EvaluationKit/TB-2.1/requirements/eval.txt ./PSWF/Harness/Terminus-2 ./PSWF/EvaluationKit/TB-2.1
~/.venvs/lar-eval/bin/python -m pswf_harbor_patches --tokenizer-dir $MODEL

# Initialize the TB-2.1 containers
~/.venvs/lar-eval/bin/python -m tb21_eval.prepare --work-dir $WORK
sudo ~/.venvs/lar-eval/bin/python -m tb21_eval.netguard up --work-dir $WORK
~/.venvs/lar-eval/bin/python -m tb21_eval.netguard probe --work-dir $WORK

# Run the evaluation
~/.venvs/lar-serve/bin/python -m tb21_eval.serve up --model $MODEL --work-dir $WORK
~/.venvs/lar-eval/bin/python -m tb21_eval.make_job --model $MODEL --work-dir $WORK --run-dir $WORK/runs/x5 --n-attempts 5 --n-concurrent 40
setsid nohup ~/.venvs/lar-eval/bin/harbor run -c $WORK/runs/x5/job.json --env-file $WORK/runs/x5/harbor.env > $WORK/runs/x5/harbor.log 2>&1 &
# ~16 h in the background; score at any time (if harbor was interrupted: ~/.venvs/lar-eval/bin/python -m tb21_eval.resume --run-dir $WORK/runs/x5)
~/.venvs/lar-eval/bin/python -m tb21_eval.score $WORK/runs/x5

@misc{wang2026lean4agenticrl,
      title={Lean4AgenticRL: Advancing Agentic RL with Verifiable Multi-level Process Rewards}, 
      author={Ruida Wang and Gerald Friedland and Tong Zhang and Chris Kong},
      year={2026},
      eprint={2610.22448},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2610.22448}, 
}