Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models
arXiv:2510.11683v3 Announce Type: replace Abstract: A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) is the intractability of their likelihood functions, which are essential for the RL objective, necessitating corresponding approximation during training. While existing methods approximate the log-likelihoods by their evidence lower bounds (ELBOs) via customized Monte Carlo (MC) sampling, they incur significant memory overhead due to the need to retain all MC samples for the gradient computation of non-linear terms in the RL objective, and thus
The continuous drive to scale large language models coincides with the increasing computational demands of reinforcement learning methods and the rise of diffusion models.
Improving memory efficiency in training advanced AI models directly impacts the feasibility and cost of developing more powerful and complex AI systems, making them accessible to a wider range of researchers and applications.
This research introduces methods to overcome significant memory bottlenecks in training diffusion large language models with reinforcement learning, potentially accelerating their development and deployment.
- · AI researchers and developers
- · Cloud providers offering AI infrastructure
- · Companies implementing advanced AI models
- · Entities reliant on highly specialized, expensive compute for state-of-the-art R
More memory-efficient RL for dLLMs will enable training larger and more sophisticated models on existing or more accessible hardware.
The reduced computational barrier could lead to faster cycles of innovation and deployment of advanced generative AI in various domains.
Democratization of such powerful AI tools could foster new applications and business models currently constrained by resource intensiveness.
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Read at arXiv cs.LG