activity
20202026
most citedDo the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark

28 citations · 111 across the 25 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

eess.AS2026

SpeechEditBench: A Bilingual Multi-Attribute Benchmark for Instruction-Guided Speech Editing

Hanlin Zhang, Daxin Tan, Dehua Tao +3

Instruction-guided speech editing requires a model to modify specified speech attributes while preserving non-target characteristics. Despite rapid progress in Speech Large Languag…

cs.CL2026

Peer-Predictive Self-Training for Language Model Reasoning

Shi Feng, Hanlin Zhang, Fan Nie +2

Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Training (PST), a label-free fin…

cs.LG2026

Next-token Preference: Scaling Unsupervised Reward Modeling

Jingxuan Fan, Yueying Li, Zhenting Qi +4

Learning from feedback advances the capabilities and safety of frontier models, but preference supervision remains costly and difficult to scale. We introduce next-token preference…

cs.LG2026

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis +1

Machine learning model performance improvements tend to arise from competition and application. For deployment, we consider prescriptive scaling laws: given a pre-training compute…

cs.LG2026

Weight Decay Improves Language Model Plasticity

Tessa Han, Sebastian Bordt, Hanlin Zhang +1

Large language models are typically trained in two broad phases: pretraining to produce a base model, followed by further training to improve downstream performance. However, hyper…