8 papers
Lightning OPD 2.0: Mitigating Style Bias in Cross-Teacher On-Policy Distillation for Large Reasoning Models
Yecheng Wu, Song Han, Han Cai
The paper proposes Lightning OPD 2.0, a method that reduces style‑related bias when using on‑policy distillation across different teacher models, improving performance on mathemati…
Hide to Guide: Learning via Semantic Masking
Ruitao Liu, Qinghao Hu, Alex Hu +6
Reinforcement learning with verifiable rewards (RLVR) has become a powerful paradigm for improving language models on reasoning-intensive tasks, but its effectiveness is often limi…
Pelican-Unify 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action
Yi Zhang, Yinda Chen, Che Liu +26
We present Pelican-Unify 1.0, the first embodied foundation model trained according to the principle of unification. Pelican-Unify 1.0 uses a single VLM as a unified understanding…
Lightning OPD: Efficient Post-Training for Large Reasoning Models with Offline On-Policy Distillation
Yecheng Wu, Song Han, Hai Cai
On-policy distillation (OPD) is an effective post-training paradigm for large language models but requires a live teacher server throughout training, resulting in substantial infra…
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
Ruihan Yang, Qinxi Yu, Yecheng Wu +12
Real robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamenta…
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer
Yecheng Wu, Junyu Chen, Zhuoyang Zhang +7
We introduce DC-AR, a novel masked autoregressive (AR) text-to-image generation framework that delivers superior image generation quality with exceptional computational efficiency.…