arXiv:2503.00539v2 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs). However, existing RLHF methods are non-robust, and their performance deteriorates if the downstream task differs significantly from the preference dataset used in fine-tuning. In order to mitigate this problem, we introduce a distributionally robust RLHF for fine-tuning LLMs. In particular, our goal is to ensure that a fine-tuned model retains its performance even when the distribution of prompts significan
The rapid deployment of LLMs and their fine-tuning through RLHF has exposed robustness issues, particularly when models encounter out-of-distribution data, making resilient methods critical.
Non-robustness in LLMs trained with human feedback can lead to unreliable performance in real-world applications, undermining trust and limiting their utility across diverse scenarios.
The introduction of distributionally robust RLHF moves LLM fine-tuning towards more reliable and adaptable models, reducing the risk of performance degradation in varied deployment environments.
- · AI developers
- · LLM users (enterprises)
- · AI safety researchers
- · Developers of non-robust RLHF methods
- · Applications with narrow, domain-specific preference datasets
LLMs become more reliable and adaptable to various real-world prompts and tasks beyond their initial training data.
Increased confidence in deploying LLMs in critical applications where performance stability across different distributions is paramount.
The development of more generalized AI agents capable of maintaining high performance even when encountering novel situations, reducing the need for constant fine-tuning.
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Read at arXiv cs.LG