Validity-calibrated reasoning distillation improves transfer of reasoning skills by modulating updates based on relative local validity of next steps instead of enforcing full trajectory imitation.
Forty-first International Conference on Machine Learning , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
CLIPer uses classifier guidance during inference to personalize LLM generations across single and multi-dimensional user preferences without extensive fine-tuning.
SmolLM2 is a 1.7B-parameter language model that outperforms Qwen2.5-1.5B and Llama3.2-1B after overtraining on 11 trillion tokens using custom FineMath, Stack-Edu, and SmolTalk datasets in a multi-stage pipeline.
citing papers explorer
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Validity-Calibrated Reasoning Distillation
Validity-calibrated reasoning distillation improves transfer of reasoning skills by modulating updates based on relative local validity of next steps instead of enforcing full trajectory imitation.
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CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
CLIPer uses classifier guidance during inference to personalize LLM generations across single and multi-dimensional user preferences without extensive fine-tuning.
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SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
SmolLM2 is a 1.7B-parameter language model that outperforms Qwen2.5-1.5B and Llama3.2-1B after overtraining on 11 trillion tokens using custom FineMath, Stack-Edu, and SmolTalk datasets in a multi-stage pipeline.