180 citations · 284 across the 30 of their papers we have counts for
11 papers · 1 filter
DeepPrune: Parallel Scaling without Inter-trace Redundancy
Shangqing Tu, Yaxuan Li, Yushi Bai +2
Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultane…
SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
Haoming Wen, Yushi Bai, Juanzi Li +1
We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved Compression, a simple yet effective RL approach for Large Reasoning Models (LRMs) that enables more eff…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models
Yuhao Wu, Yushi Bai, Zhiqiang Hu +2
Long-form text generation remains a significant challenge for large language models (LLMs), particularly in maintaining coherence, ensuring logical consistency, and preserving text…
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning
Yuhao Wu, Yushi Bai, Zhiqiang Hu +2
Ultra-long generation by large language models (LLMs) is a widely demanded scenario, yet it remains a significant challenge due to their maximum generation length limit and overall…
How do Transformers Learn Implicit Reasoning?
Jiaran Ye, Zijun Yao, Zhidian Huang +8
Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly -- producing correct answers without explicitly verbalizing intermediate steps --…
MMGeoLM: Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Models
Kai Sun, Yushi Bai, Zhen Yang +4
Large Multimodal Models (LMMs) typically build on ViTs (e.g., CLIP), yet their training with simple random in-batch negatives limits the ability to capture fine-grained visual diff…
An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes
Ji Qi, Yuan Yao, Yushi Bai +4
Large Multimodal Models (LMMs) uniformly perceive video frames, creating computational inefficiency for videos with inherently varying temporal information density. This paper pres…
Shifting Long-Context LLMs Research from Input to Output
Yuhao Wu, Yushi Bai, Zhiqing Hu +4
Recent advancements in long-context Large Language Models (LLMs) have primarily concentrated on processing extended input contexts, resulting in significant strides in long-context…
LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
Shangqing Tu, Yucheng Wang, Daniel Zhang-Li +8
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000…
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
Yushi Bai, Shangqing Tu, Jiajie Zhang +9
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world…