SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.
The power of scale for parameter-efficient prompt tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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IMAX trains soft prefixes with an InfoMax reward to drive diverse exploration in RLVR, yielding up to 11.60% gains in Pass@4 over standard RLVR across model scales.
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SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.
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How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors
IMAX trains soft prefixes with an InfoMax reward to drive diverse exploration in RLVR, yielding up to 11.60% gains in Pass@4 over standard RLVR across model scales.