1 citations · 2 across the 7 of their papers we have counts for
7 papers
Inference-Time Code Selection via Symbolic Equivalence Partitioning
David Cho, Yifan Wang, Fanping Sui +1
Sampling multiple candidate programs at inference time is an effective way to improve LLM code generation. However, its benefit depends on reliably selecting a correct solution fro…
SARL: Label-Free Reinforcement Learning by Rewarding Reasoning Topology
Yifan Wang, Bolian Li, David Cho +3
Reinforcement learning is critical to improving large reasoning models, but its success relies heavily on verifiable rewards (RLVR), making it hard to use in open-ended domains whe…
LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X
Shuo Xing, Junyuan Hong, Yifan Wang +5
We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To unveil junk effects, we…
DRIFT: Learning from Abundant User Dissatisfaction in Real-World Preference Learning
Yifan Wang, Bolian Li, Junlin Wu +5
Real-world large language model deployments (e.g., conversational AI systems, code generation assistants) naturally generate abundant implicit user dissatisfaction (DSAT) signals,…
More is Less: The Pitfalls of Multi-Model Synthetic Preference Data in DPO Safety Alignment
Yifan Wang, Runjin Chen, Bolian Li +7
Aligning large language models (LLMs) with human values is an increasingly critical step in post-training. Direct Preference Optimization (DPO) has emerged as a simple, yet effecti…
Deconvolving Complex Neuronal Networks into Interpretable Task-Specific Connectomes
Yifan Wang, Vikram Ravindra, Ananth Grama
Task-specific functional MRI (fMRI) images provide excellent modalities for studying the neuronal basis of cognitive processes. We use fMRI data to formulate and solve the problem…
Cascade Reward Sampling for Efficient Decoding-Time Alignment
Bolian Li, Yifan Wang, Anamika Lochab +2
Aligning large language models (LLMs) with human preferences is essential for their applications. Recently, decoding-time alignment has emerged as an effective plug-and-play techni…