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Found 4 papers, 1 papers with code
L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models
To overcome these challenges, we propose leap multi-token prediction~(L-MTP), an innovative token prediction method that extends the capabilities of multi-token prediction (MTP) by introducing a leap-based mechanism.
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
In addition, strategically scaling the model sizes or reasoning steps can effectively enhance the performance.
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
In this paper, to handle the problem and address the limitations of prior works, we propose a representation calibration method RCAL.
Tackling Instance-Dependent Label Noise with Dynamic Distribution Calibration
In this paper, to address the distribution shift in learning with instance-dependent label noise, a dynamic distribution-calibration strategy is adopted.
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