aiqbench analysis
Ready2Blend addresses the critical challenge of continual alignment in large language models by enabling adaptation to new requirements without retraining the model backbone or forgetting prior behaviors. This method leverages a modular prompt bank and composability regularization to achieve performance comparable to post-training approaches while significantly reducing training time and computational overhead. The research demonstrates a viable path toward flexible, efficient AI systems that can dynamically blend natural language instructions with learned alignment strategies.
Ready2Blend: From Natural-Language Instructions to Composable Alignment Prompts
arXiv:2609.39365v1 Announce Type: cross Abstract: Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are flexible and composable but offer only indirect control, whereas post-training provides stronger adaptation at the cost of repeated parameter updates. We introduce Ready2Blend, which combines the flexibility of natural language with learned alignment. AlignFormer maps each requirement to a fixed-length alignment prompt stored in a modular prompt bank, while the backbone and prior prompts remain frozen. Composability regularization transfers the semantic geometry of textual requirements into prompt space, enabling inference-time blending and reweighting. Across two practical continual alignment settings, Ready2Blend is the only frozen-backbone method that matches post-training-based alignment methods, reaching $93.1$-$98.5\%$ of a joint-training reference with competitive retention, while requiring only a few prompt tokens and up to $4.3\times$ less training time. Its modular design further enables weighted personalization and order-free composition without retraining. Code will be released upon acceptance.
Abstract:Continual alignment requires LLMs to adapt to new requirements without forgetting previously acquired behaviors. Natural-language instructions are flexible and composable but offer only indirect control, whereas post-training provides stronger adaptation at the cost of repeated parameter updates. We introduce Ready2Blend, which combines the flexibility of natural language with learned alignment. AlignFormer maps each requirement to a fixed-length alignment prompt stored in a modular prompt bank, while the backbone and prior prompts remain frozen. Composability regularization transfers the semantic geometry of textual requirements into prompt space, enabling inference-time blending and reweighting. Across two practical continual alignment settings, Ready2Blend is the only frozen-backbone method that matches post-training-based alignment methods, reaching $93.1$-$98.5\%$ of a joint-training reference with competitive retention, while requiring only a few prompt tokens and up to $4.3\times$ less training time. Its modular design further enables weighted personalization and order-free composition without retraining. Code will be released upon acceptance.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.39365 [cs.CL]
(or arXiv:2609.39365v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.39365 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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