1 citations · 2 across the 14 of their papers we have counts for
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SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models
Yifu Huo, Chenglong Wang, Ziming Zhu +9
Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends…
DaPT: A Dual-Path Framework for Multilingual Multi-hop Question Answering
Yilin Wang, Yuchun Fan, Jiaoyang Li +5
Retrieval-augmented generation (RAG) systems have made significant progress in solving complex multi-hop question answering (QA) tasks in the English scenario. However, RAG systems…
APR: Penalizing Structural Redundancy in Large Reasoning Models via Anchor-based Process Rewards
Kaiyan Chang, Chenwei Zhu, Yingfeng Luo +7
Test-Time Scaling (TTS) has significantly enhanced the capabilities of Large Reasoning Models (LRMs) but introduces a critical side-effect known as Overthinking. We conduct a preli…
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams
Chenglong Wang, Canjia Li, Xingzhao Zhu +9
Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolutio…
Probing Preference Representations: A Multi-Dimensional Evaluation and Analysis Method for Reward Models
Chenglong Wang, Yifu Huo, Yang Gan +10
Previous methods evaluate reward models by testing them on a fixed pairwise ranking test set, but they typically do not provide performance information on each preference dimension…
GRAM: A Generative Foundation Reward Model for Reward Generalization
Chenglong Wang, Yang Gan, Yifu Huo +8
In aligning large language models (LLMs), reward models have played an important role, but are standardly trained as discriminative models and rely only on labeled human preference…
Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models
Yongyu Mu, Hengyu Li, Junxin Wang +7
Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes p…
LRHP: Learning Representations for Human Preferences via Preference Pairs
Chenglong Wang, Yang Gan, Yifu Huo +7
To improve human-preference alignment training, current research has developed numerous preference datasets consisting of preference pairs labeled as "preferred" or "dispreferred".…
Cross-layer Attention Sharing for Pre-trained Large Language Models
Yongyu Mu, Yuzhang Wu, Yuchun Fan +9
To enhance the efficiency of the attention mechanism within large language models (LLMs), previous works primarily compress the KV cache or group attention heads, while largely ove…