CalibForge-35B-A3B

📄 Paper · 💻 Repository · 🤗 Dataset · 🤖 30B Model

CalibForge-35B-A3B is a terminal-agent model fine-tuned from Qwen3.5-35B-A3B using full-parameter, multi-turn supervised fine-tuning. Its training trajectories were distilled from 5,431 calibrated terminal tasks spanning 16 domains. CalibForge uses verified solver outcomes and full trajectories to revise executable terminal tasks toward a solver-relative learnable zone. See the paper for the task-construction pipeline.

Evaluation Results

Model	Terminal-Bench 2.0 Acc. (%) ↑	SWE-bench Pro Resolved (%) ↑	Doc2Repo Pass Rate (%) ↑
Qwen3.5-35B-A3B (base)	39.10 ± 1.09	41.29	44.92 ± 1.14
CalibForge-35B-A3B (this model)	47.57 ± 0.99	44.32	48.77 ± 0.90

Terminal-Bench 2.0 and Doc2Repo results are reported as mean ± SEM over three runs; SWE-bench Pro is evaluated once. Terminal-Bench 2.0 uses CalibForge-Eval with a 500-step limit, a one-hour per-task timeout, and sandboxes capped at 16 CPUs and 32 GB RAM. SWE-bench Pro and Doc2Repo use their official evaluation scaffolds; corresponding AweAgent recipes are available for SWE-bench Pro and Doc2Repo through BeyondSWE. See the paper for comparisons with other training-data sources and for the complete evaluation and decontamination protocols.

Usage with SGLang

Install a recent SGLang release with Qwen3.5 MoE support, then launch an OpenAI-compatible server:

uv venv --python 3.12 .venv
source .venv/bin/activate
uv pip install "sglang[all]"

python -m sglang.launch_server \
  --model-path AweAI-Team/CalibForge-35B-A3B \
  --tp 8 \
  --dp 1 \
  --trust-remote-code \
  --enable-metrics \
  --max-running-requests 40 \
  --reasoning-parser qwen3 \
  --tool-call-parser qwen3_coder \
  --mem-fraction-static 0.85 \
  --host :: \
  --port 40003

Model Details

Property	Value
Backbone	Qwen3.5-35B-A3B
Precision	BF16
Configured maximum positions	262,144
Training method	Full-parameter, multi-turn SFT
Training context length	131,072 tokens
Training epochs	10
Optimizer	AdamW (β₁=0.9, β₂=0.999)
Learning rate	1.0e-5, cosine schedule, 0.05 warmup ratio
Global batch size	128
Training hardware	64 × NVIDIA H20 GPUs

License

The model is released under the Apache License 2.0.

Citation

If you use this model, please cite the CalibForge paper:

@misc{meng2026calibforge,
  title         = {CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks},
  author        = {Fanzhe Meng and Guoxin Chen and Jiale Zhao and Shuang Sun and Zhiyu Lin and Wayne Xin Zhao and Ruihua Song and Ji-Rong Wen and Kai Jia},
  year          = {2026},
  eprint        = {2608.06352},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2608.06352}
}