clap-qwen3vl4b-libero

clap-qwen3vl4b-libero

LIBERO post-trained checkpoint of CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos.

A single generalist policy across all four LIBERO suites (Spatial / Object / Goal / Long), obtained by post-training LinShan/clap-qwen3vl4b with a rectified-flow continuous action head (CLAP-RF) attending to the frozen NTP backbone's KV cache, regularized by reverse-KL Knowledge Matching (KM) toward the frozen NTP reference. Trained for 30k steps at batch size 128 on the union of the four LIBERO suites.

LIBERO performance

Links

• 💻 Code: https://github.com/LinShan-Bin/OpenCLAP
• 📄 Paper: https://arxiv.org/abs/2601.04061
• 🌐 Project page: https://lin-shan.com/CLAP/

Usage

hf download LinShan/clap-qwen3vl4b-libero \
  --local-dir ./ckpts/Checkpoints/libero_clap_s3_l32_qwen3vl4b_km_l16

Evaluation uses a client–server split (policy server in the openclap env, simulator client in a separate libero env):

# Terminal 1 (openclap env)
bash examples/LIBERO/eval_files/run_policy_server.sh

# Terminal 2 (libero env)
bash examples/LIBERO/eval_files/eval_libero.sh

Citation

@article{zhang2026clap,
  title={CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos},
  author={Zhang, Chubin and Wang, Jianan and Gao, Zifeng and Su, Yue and Dai, Tianru and Zhou, Cai and Lu, Jiwen and Tang, Yansong},
  journal={arXiv preprint arXiv:2601.04061},
  year={2026}
}