In current occasions, Massive Language Fashions (LLMs) have gained reputation for his or her potential to answer consumer queries in a extra human-like method, achieved by reinforcement studying. Nevertheless, aligning these LLMs with human preferences in reinforcement studying from human suggestions (RLHF) can result in a phenomenon often known as reward hacking. This happens when LLMs exploit flaws within the reward mannequin (RM), reaching excessive rewards with out fulfilling the underlying aims, as illustrated in Determine 1(b). Reward hacking raises considerations reminiscent of 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). Nevertheless, these strategies face effectivity points, reliability considerations, and wrestle with desire inconsistencies.
To sort out 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 sturdy RM. WARM combines a number of RMs by linear interpolation within the weight house, offering advantages reminiscent of effectivity, improved reliability beneath distribution shifts, and enhanced robustness to label corruption. The variety 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 displays superiority beneath distribution shifts. The paper introduces the idea of linear mode connectivity (LMC) as a key consider WARM’s success, demonstrating its potential 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:
- Remark 1 (LMC): The accuracy of the interpolated mannequin is at the least nearly as good because the interpolation of the person accuracies.
- Remark 2 (WA and ENS): Weight averaging and prediction ensembling carry out equally.
- Remark 3 (WA and ENS): The accuracy beneficial properties of WA over ENS develop as information strikes away from the coaching distribution.
The advantages of WARM lengthen past its major targets. It aligns with the updatable machine studying paradigm, permitting parallelization in federated studying eventualities. WARM may contribute to privateness and bias mitigation by lowering memorization of personal preferences. The tactic exhibits potential for combining RMs educated on completely different datasets, supporting iterative and evolving preferences. Additional exploration contains 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 fully eradicate spurious correlations or biases in desire information, suggesting the necessity for added strategies for a complete answer. Lastly, WARM focuses on enhancing reward modeling and ought to be thought-about throughout the broader context of accountable AI to deal with security dangers from misalignment.
In conclusion, Weight Averaged Reward Fashions (WARM) provide a promising answer to challenges in reward modeling, enhancing alignment in RLHF. The paper’s empirical outcomes and theoretical insights place WARM as a helpful contribution towards creating extra aligned, clear, and efficient AI methods.
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