Selective State-Space Adaptation and Retrieval for Language Model Reasoning

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Was passiert ist

Researchers propose MaLoRA and MaRA adapters that use state-space recurrence to improve language model reasoning, achieving up to 18.2% relative gains over standard LoRA on multi-hop reasoning tasks.

Warum es zählt

Engineers fine-tuning frozen language models for reasoning tasks should consider this when standard low-rank adaptation leaves accuracy gaps on complex question-answering benchmarks.

Achtung

Paper is recent preprint with no reported code availability yet; gains tested only on three specific models and two reasoning benchmarks, generalization unclear.