24 citations · 119 across the 18 of their papers we have counts for
5 papers · 1 filter
RLPR: Extrapolating RLVR to General Domains without Verifiers
Tianyu Yu, Bo Ji, Shouli Wang +9
Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates promising potential in advancing the reasoning capabilities of LLMs. However, its success remains largely confine…
Process Reinforcement through Implicit Rewards
Ganqu Cui, Lifan Yuan, Zefan Wang +22
Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…
Free Process Rewards without Process Labels
Lifan Yuan, Wendi Li, Huayu Chen +6
Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing…
Noise Contrastive Alignment of Language Models with Explicit Rewards
Huayu Chen, Guande He, Lifan Yuan +3
User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimiz…
A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks
Ganqu Cui, Lifan Yuan, Bingxiang He +3
Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a backdoor in the training phase, the adversary could control model predictions via predefined…