Papers with gradient accumulation
Towards efficient self-supervised representation learning in speech processing (2024.findings-eacl)
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| Challenge: | Existing models require several GPUs for days to pretrain, generating environmental concerns because of their high energy consumption. |
| Approach: | They propose an efficient self-supervised model that uses a single GPU during 24 to 48 hours of pretraining to address high computational costs. |
| Outcome: | The proposed model represents two orders of magnitude better than existing models. |
CONGRAD: Conflicting Gradient Filtering for Multilingual Preference Alignment (2026.eacl-long)
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Jiangnan Li, Thuy-Trang Vu, Christian Herold, Amirhossein Tebbifakhr, Shahram Khadivi, Gholamreza Haffari
| Challenge: | Naive joint training of large language models can suffer from negative interference. |
| Approach: | They propose a filtering method that aggregates cross-lingually beneficial gradients and filters for those with high cross-linguistic affinity. |
| Outcome: | The proposed method outperforms baselines in both seen and unseen languages with minimal alignment tax. |
Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for reinforcement learning (RL) require a large sample size to be implemented. |
| Approach: | They propose a memory-efficient RL algorithm that maximizes a lower bound of the ELBO-based objective. |
| Outcome: | Experiments show that BGPO outperforms previous RL algorithms for diffusion large language models in math problem solving, code generation, and planning tasks. |