25 citations · 59 across the 33 of their papers we have counts for
33 papers
Learning Latent Reasoning Traces for Scalar Reward Models End-to-End
Sanwoo Lee, Clive Bai, Hsiu-Yuan Huang +3
Reward models (RMs) are central to aligning large language models with human preferences via reinforcement learning. Although traditional scalar RMs enable efficient and probabilis…
TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
Heming Zou, Qi Wang, Yun Qu +9
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models. However, rollout-intensive poli…
ADWIN: Adaptive Windows for Horizon-Aware On-Policy Distillation
Kun Liang, Chenming Tang, Clive Bai +3
On-policy distillation (OPD) transfers reasoning behavior by training a student on teacher feedback along student-generated trajectories, but standard full-rollout training ties ev…
RLVR Datasets and Where to Find Them: Tracing Data Lineage for Better Training Data
Hsiu-Yuan Huang, Weijie Liu, Chenming Tang +5
The proliferation of Reinforcement Learning from Verifiable Rewards (RLVR) datasets has exacerbated provenance collapse due to unclear lineage among existing datasets. To bridge th…
Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation
Yuchen Cai, Ding Cao, Liang Lin +9
On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, existing studies largely attribute this advantage to denser and…
Listwise Policy Optimization: Group-based RLVR as Target-Projection on the LLM Response Simplex
Yun Qu, Qi Wang, Yixiu Mao +11
Reinforcement learning with verifiable rewards (RLVR) has become a standard approach for large language models (LLMs) post-training to incentivize reasoning capacity. Among existin…
Democratizing Tool Learning with Environments Fully Simulated by a Free 8B Language Model
Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3
Reinforcement learning has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments. Existing approaches either rely o…
Misrouter: Exploiting Routing Mechanisms for Input-Only Attacks on Mixture-of-Experts LLMs
Zekun Fei, Zihao Wang, Weijie Liu +4
Mixture-of-Experts (MoE) architectures have emerged as a leading paradigm for scaling large language models through sparse, routing-based computation. However, this design introduc…
Learning beyond Teacher: Generalized On-Policy Distillation with Reward Extrapolation
Wenkai Yang, Weijie Liu, Ruobing Xie +3
On-policy distillation (OPD), which aligns the student with the teacher's logit distribution on student-generated trajectories, has demonstrated strong empirical gains in improving…
Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models
Xin Xu, Clive Bai, Kai Yang +8
Large-scale verifiable prompts underpin the success of Reinforcement Learning with Verifiable Rewards (RLVR), but they contain many uninformative examples and are costly to expand…
Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models
Yun Qu, Qi Wang, Yixiu Mao +8
Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-intensive optimization. Online prompt…
ORBIT: On-policy Exploration-Exploitation for Controllable Multi-Budget Reasoning
Kun Liang, Clive Bai, Xin Xu +5
Recent Large Reasoning Models (LRMs) achieve strong performance by leveraging long-form Chain-of-Thought (CoT) reasoning, but uniformly applying overlong reasoning at inference tim…
VI-MMRec: Similarity-Aware Training Cost-free Virtual User-Item Interactions for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Although existing multimodal recommendation models have shown promising performance, their effectiveness continues to be limited by the pervasive data sparsity problem. This proble…
EntroPIC: Towards Stable Long-Term Training of LLMs via Entropy Stabilization with Proportional-Integral Control
Kai Yang, Xin Xu, Yangkun Chen +5
Long-term training of large language models (LLMs) requires maintaining stable exploration to prevent the model from collapsing into sub-optimal behaviors. Entropy is crucial in th…
LaSeR: Reinforcement Learning with Last-Token Self-Rewarding
Wenkai Yang, Weijie Liu, Ruobing Xie +4
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a core paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). To address t…
Think Outside the Policy: In-Context Steered Policy Optimization
Hsiu-Yuan Huang, Chenming Tang, Weijie Liu +3
Existing Reinforcement Learning from Verifiable Rewards (RLVR) methods, such as Group Relative Policy Optimization (GRPO), have achieved remarkable progress in improving the reason…
Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error
Chenming Tang, Hsiu-Yuan Huang, Weijie Liu +3
Reinforcement learning with verifiable rewards (RLVR) has significantly boosted the reasoning capability of language models (LMs). However, existing RLVR approaches train LMs based…
Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Ruiqi He, Zekun Fei, Jiaqi Li +5
Vector Database (VDB) can efficiently index and search high-dimensional vector embeddings from unstructured data, crucially enabling fast semantic similarity search essential for m…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
Weijie Liu, Ziwei Zhan, Carlee Joe-Wong +5
Non-independent and identically distributed (Non-IID) data across edge clients have long posed significant challenges to federated learning (FL) training in edge computing environm…
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Ziwei Zhan, Wenkuan Zhao, Yuanqing Li +6
Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learnin…
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
Han Liang, Ziwei Zhan, Weijie Liu +3
Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis, primarily f…
Pro-Prophet: A Systematic Load Balancing Method for Efficient Parallel Training of Large-scale MoE Models
Wei Wang, Zhiquan Lai, Shengwei Li +5
The size of deep learning models has been increasing to enhance model quality. The linear increase in training computation budget with model size means that training an extremely l…
MemLong: Memory-Augmented Retrieval for Long Text Modeling
Weijie Liu, Zecheng Tang, Juntao Li +2
Recent advancements in Large Language Models (LLMs) have yielded remarkable success across diverse fields. However, handling long contexts remains a significant challenge for LLMs…