3 citations · 13 across the 27 of their papers we have counts for
7 papers · 1 filter
Stabilizing Long-term Multi-turn Reinforcement Learning with Gated Rewards
Zetian Sun, Dongfang Li, Zhuoen Chen +2
Reward sparsity in long-horizon reinforcement learning (RL) tasks remains a significant challenge, while existing outcome-based reward shaping struggles to define meaningful immedi…
Improving Value-based Process Verifier via Low-Cost Variance Reduction
Zetian Sun, Dongfang Li, Baotian Hu +1
Large language models (LLMs) have achieved remarkable success in a wide range of tasks. However, their reasoning capabilities, particularly in complex domains like mathematics, rem…
Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?
Zetian Sun, Dongfang Li, Xuhui Chen +2
The alignment of language models~(LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize t…
KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
Xinping Zhao, Xinshuo Hu, Zifei Shan +14
Recent advancements in Large Language Models (LLMs)-based text embedding models primarily focus on data scaling or synthesis, yet limited exploration of training techniques and dat…
Take Off the Training Wheels Progressive In-Context Learning for Effective Alignment
Zhenyu Liu, Dongfang Li, Xinshuo Hu +4
Recent studies have explored the working mechanisms of In-Context Learning (ICL). However, they mainly focus on classification and simple generation tasks, limiting their broader a…
Improving Value-based Process Verifier via Structural Prior Injection
Zetian Sun, Dongfang Li, Baotian Hu +2
In the Large Language Model(LLM) reasoning scenario, people often estimate state value via Monte Carlo sampling. Though Monte Carlo estimation is an elegant method with less induct…
KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
Xinshuo Hu, Zifei Shan, Xinping Zhao +10
As retrieval-augmented generation prevails in large language models, embedding models are becoming increasingly crucial. Despite the growing number of general embedding models, pri…