Traditional approaches to training LLM-based judges depend on large, labeled datasets, repeated fine-tuning, or prompt-based heuristics, all of which are expensive to maintain and slow to adapt as models, prompts, and business requirements change.
As a result, AI evaluation often remains manual and periodic, limiting enterprises’ ability to safely iterate and deploy models at scale, the team wrote in a blog post.
MemAlign’s memory-driven alternative to brute-force retraining
In contrast, MemAlign uses a dual memory system that replaces brute-force retraining with memory-driven alignment based on human feedback from human subject matter experts, although fewer in number and frequency than conventional training methods.
