The authors developed Ready2Blend around AlignFormer, a variant of Q-Former that maps a natural-language requirement and its supervision into a fixed-length continuous prompt.
Learned prompts combine evolving LLM requirements without changing model weights
Ready2Blend converts individual requirements into modular continuous prompts that can be blended and reweighted at inference.
Big Tech
Jeesu Jung · Hwan Chang · Juseon Do · Jeonghwan Choi · Jinho Choo · Sungwoo Nam · +2 more
Korea Advanced Institute of Science Technology · Samsung SDS
Research Digest··2 min read
Thread:Agent Harness Optimization
Jung and colleagues propose a continual alignment method that learns a small prompt for each new behavioral requirement while freezing the language model and all previously learned prompts.
Why this paper
From Samsung SDS and Korea Advanced Institute of Science Technology · Part of Agent Harness Optimization, now 95 papers
In one line
Ready2Blend learns alignment requirements as composable prompts over a frozen backbone, matching post-training methods with less training time.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
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