2 citations · 7 across the 33 of their papers we have counts for
14 papers · 1 filter
Agensh: Scaling Organizational Intelligence to 1,024 Agents
Zhihao Zhan, Ting Song, Li Dong +4
A multi-agent system can reduce latency on complex tasks by executing work concurrently. Several pioneering harness frameworks support multi-agent systems. However, the scalability…
Multi-Turn On-Policy Distillation with Prefix Replay
Baohao Liao, Hanze Dong, Christof Monz +3
We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn…
VibeVoice-ASR-BitNet Technical Report
Songchen Xu, Ting Song, Shaohan Huang +10
We present VibeVoice-ASR-BitNet, a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs. We apply heterogeneous quantization tailored to the computati…
LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
Tianzhu Ye, Li Dong, Guanheng Chen +4
Reinforcement learning (RL) on open-ended tasks compresses an LLM's rubric-based evaluation into a scalar reward, discarding rich textual feedback and conflating responses with dis…
You Only Index Once: Cross-Layer Sparse Attention with Shared Routing
Yutao Sun, Yanqi Zhang, Li Dong +2
Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate long intermediate chains of t…
Universal YOCO for Efficient Depth Scaling
Yutao Sun, Li Dong, Tianzhu Ye +3
The rise of test-time scaling has remarkably boosted the reasoning and agentic proficiency of Large Language Models (LLMs). Yet, standard Transformers struggle to scale inference-t…
RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
Shaopeng Fu, Xingxing Zhang, Li Dong +2
While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on sin…
On-Policy Context Distillation for Language Models
Tianzhu Ye, Li Dong, Xun Wu +2
Context distillation enables language models to internalize in-context knowledge into their parameters. In our work, we propose On-Policy Context Distillation (OPCD), a framework t…
Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models
Zongqian Li, Shaohan Huang, Zewen Chi +5
Modern code generation models exhibit longer outputs, accelerated capability growth, and changed training dynamics, rendering traditional training methodologies, algorithms, and da…
SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
Hanyong Shao, Yingbo Hao, Ting Song +10
NVIDIA's 2:4 Sparse Tensor Cores deliver 2x throughput but demand strict 50% pruning -- a ratio that collapses LLM reasoning accuracy (Qwen3: 54% to 15%). Milder patter…
Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
Di Zhang, Xun Wu, Shaohan Huang +9
Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they ha…
Online Experiential Learning for Language Models
Tianzhu Ye, Li Dong, Qingxiu Dong +3
The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the rich experience accumulated dur…
Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
Yao Tang, Li Dong, Yaru Hao +3
Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast,…
Computer Environments Elicit General Agentic Intelligence in LLMs
Daixuan Cheng, Shaohan Huang, Yuxian Gu +6
Agentic intelligence in large language models (LLMs) requires not only model intrinsic capabilities but also interactions with external environments. Equipping LLMs with computers…