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https://arxiv.org/abs/2404.01730

https://arxiv.org/abs/2402.01694  ARGS: Alignment as Reward-Guided SearchAligning large language models with human objectives is paramount, yet common approaches including RLHF suffer from unstable and resource-intensive training. In response to this challenge, we introduce ARGS, Alignment as Reward-Guided Search, a novel framearxiv.orgAligning large language models with human objectives is para..

https://arxiv.org/pdf/2402.10038Reinforcement learning from human feedback (RLHF) has been extensively employed to align large language models with user intent. However, proximal policy optimization (PPO) based RLHF is occasionally unstable requiring significant hyperparameter finetuning, and computationally expensive to maximize the estimated reward during alignment. Recently, direct preference..

https://arxiv.org/abs/2404.11912

https://arxiv.org/pdf/2405.00675Standard reinforcement learning from human feedback (RLHF) approaches relying on parametric models like the Bradley-Terry model fall short in capturing the intransitivity and irrationality in human preferences. Recent advancements suggest that directly working with preference probabilities can yield a more accurate reflection of human preferences, enabling more fl..

https://arxiv.org/pdf/2407.06057

https://arxiv.org/abs/2401.11206 InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model GuidanceWith the rapid development of large language models (LLMs), they are not only used as general-purpose AI assistants but are also customized through further fine-tuning to meet the requirements of different applications. A pivotal factor in the success of carxiv.orgAbstract With th..

https://arxiv.org/pdf/2410.20290The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model’s responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained model weights during a phase called post-training. While predomina..

https://arxiv.org/pdf/2406.12845https://github.com/RLHFlow/RLHF-Reward-Modeling Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences. The RLHF process typically starts by training a reward model (RM) using human preference data. Conventional RMs are trained on pairwise responses to the same user reque..

https://arxiv.org/pdf/2407.08739 Multi-modal Large Language Models (MLLMs) have recently emerged as a significant focus in academia and industry. Despite their proficiency in general multi-modal scenarios, the mathematical problem-solving capabilities in visual contexts remain insufficiently explored. We identify three key areas within MLLMs that need to be improved: visual encoding of math diag..