CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
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Overall
85
Content
95
Popularity
61
Observed public metrics from 1 member.
Merged summary
TL;DR - CritICL is an inference-time framework that uses structured failure patterns from smaller models as critique-based in-context guidance for stronger models. It improves reasoning with fewer generations and lower token costs than conventional test-time scaling approaches.
- Exploits the observation that failure modes recur across model scales within the same model family.
- CritICL-dynamic predicts input-specific failure modes and retrieves relevant critiques.
- CritICL-static applies a global failure-mode profile for consistent guidance.
- Experiments report gains over standard in-context learning and performance competitive with or better than test-time scaling methods.
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CritICL: Inference-Time Weak-to-Strong Generalization from Small Language Model Failure Modes
Public signals
Hugging Face upvotes 11
TL;DR - CritICL is an inference-time framework that uses structured failure patterns from smaller models as critique-based in-context guidance for stronger models. It improves reasoning with fewer generations and lower token costs than conventional test-time scaling approaches.
- Exploits the observation that failure modes recur across model scales within the same model family.
- CritICL-dynamic predicts input-specific failure modes and retrieves relevant critiques.
- CritICL-static applies a global failure-mode profile for consistent guidance.
- Experiments report gains over standard in-context learning and performance competitive with or better than test-time scaling methods.