Convergence for Discrete Parameter Update Schemes
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912750668611584 |
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| author | Wilson, Paul Zanasi, Fabio Constantinides, George |
| author_facet | Wilson, Paul Zanasi, Fabio Constantinides, George |
| contents | Modern deep learning models require immense computational resources, motivating research into low-precision training. Quantised training addresses this by representing training components in low-bit integers, but typically relies on discretising real-valued updates. We introduce an alternative approach where the update rule itself is discrete, avoiding the quantisation of continuous updates by design. We establish convergence guarantees for a general class of such discrete schemes, and present a multinomial update rule as a concrete example, supported by empirical evaluation. This perspective opens new avenues for efficient training, particularly for models with inherently discrete structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convergence for Discrete Parameter Update Schemes Wilson, Paul Zanasi, Fabio Constantinides, George Machine Learning Optimization and Control Modern deep learning models require immense computational resources, motivating research into low-precision training. Quantised training addresses this by representing training components in low-bit integers, but typically relies on discretising real-valued updates. We introduce an alternative approach where the update rule itself is discrete, avoiding the quantisation of continuous updates by design. We establish convergence guarantees for a general class of such discrete schemes, and present a multinomial update rule as a concrete example, supported by empirical evaluation. This perspective opens new avenues for efficient training, particularly for models with inherently discrete structure. |
| title | Convergence for Discrete Parameter Update Schemes |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2512.04051 |