In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal
Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards--a …

Aligning language models (LMs) to human preferences has emerged as a critical pursuit,
enabling these models to better serve diverse user needs. Existing methods primarily focus …

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial
factors to achieve success is aligning the LLM's output with human preferences. This …

Reward-based finetuning is crucial for aligning language policies with intended behaviors
(eg, creativity and safety). A key challenge is to develop steerable language models that …

Reinforcement learning from human feedback (RLHF) has become the leading approach for
fine-tuning large language models (LLM). However, RLHF has limitations in multi-task …

Diffusion models have emerged as the leading paradigm in generative modeling, excelling
in various applications. Despite their success, these models often misalign with human …

The rapid advancements in Vision-Language Models (VLMs) have shown great potential in
tackling mathematical reasoning tasks that involve visual context. Unlike humans who can …

Aligning large language models (LLMs) with human preferences is critical for their
deployment. Recently, decoding-time alignment has emerged as an effective plug-and-play …

Personalization of Large Language Models (LLMs) has recently become increasingly
important with a wide range of applications. Despite the importance and recent progress …

Aligning Large Language Models (LLMs) is crucial for enhancing their safety and utility.
However, existing methods, primarily based on preference datasets, face challenges such …