In current occasions, Giant Language Fashions (LLMs) have gained recognition for his or her skill to reply to person queries in a extra human-like method, achieved via reinforcement studying. Nonetheless, aligning these LLMs with human preferences in reinforcement studying from human suggestions (RLHF) can result in a phenomenon referred to as reward hacking. This happens when LLMs exploit flaws within the reward mannequin (RM), reaching excessive rewards with out fulfilling the underlying targets, as illustrated in Determine 1(b). Reward hacking raises issues similar to degraded efficiency, checkpoint choice challenges, potential biases, and, most critically, security dangers.
The first challenges recognized in designing RMs to mitigate reward hacking embrace distribution shifts and inconsistent preferences within the desire dataset. Distribution shifts come up as a consequence of coverage drift throughout RL, resulting in a deviation from the offline desire dataset. Inconsistent preferences stem from noisy binary labels, introducing low inter-labeler settlement and impacting RM robustness. To handle these challenges, current approaches have explored methods like KL regularization, lively studying, and prediction ensembling (ENS). Nonetheless, these strategies face effectivity points, reliability issues, and battle with desire inconsistencies.
To deal with these challenges, this paper proposes Weight Averaged Reward Models (WARM) (illustrated in Determine 1(a)), a easy, environment friendly, and scalable technique for acquiring a dependable and strong RM. WARM combines a number of RMs via linear interpolation within the weight house, offering advantages similar to effectivity, improved reliability beneath distribution shifts, and enhanced robustness to label corruption. The range throughout fine-tuned weights is a key contributor to the effectiveness of WARM.
WARM is in comparison with prediction ensembling (ENS), showcasing its effectivity and practicality by requiring a single mannequin at inference time, eliminating reminiscence and inference overheads. Empirical outcomes point out that WARM performs equally to ENS by way of variance discount however reveals superiority beneath distribution shifts. The paper introduces the idea of linear mode connectivity (LMC) as a key consider WARM’s success, demonstrating its skill to memorize much less and generalize higher than ensembling predictions. There are 3 observations which might be made within the experiments and are empirically confirmed in Determine 3 and 4:
- Statement 1 (LMC): The accuracy of the interpolated mannequin is at the least nearly as good because the interpolation of the person accuracies.
- Statement 2 (WA and ENS): Weight averaging and prediction ensembling carry out equally.
- Statement 3 (WA and ENS): The accuracy positive factors of WA over ENS develop as information strikes away from the coaching distribution.
The advantages of WARM prolong past its main targets. It aligns with the updatable machine studying paradigm, permitting parallelization in federated studying eventualities. WARM might contribute to privateness and bias mitigation by decreasing memorization of personal preferences. The strategy exhibits potential for combining RMs skilled on completely different datasets, supporting iterative and evolving preferences. Additional exploration consists of extending WARM to direct desire optimization methods.
Regardless of its innovation, WARM has limitations in comparison with prediction ensembling strategies, together with potential limitations in dealing with numerous architectures and uncertainty estimation. WARM doesn’t completely remove spurious correlations or biases in desire information, suggesting the necessity for added strategies for a complete resolution. Lastly, WARM focuses on enhancing reward modeling and ought to be thought-about inside the broader context of accountable AI to deal with security dangers from misalignment.
In conclusion, Weight Averaged Reward Fashions (WARM) provide a promising resolution to challenges in reward modeling, enhancing alignment in RLHF. The paper’s empirical outcomes and theoretical insights place WARM as a worthwhile contribution towards creating extra aligned, clear, and efficient AI programs.
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