arXiv:2602.05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning? Can policies using a fixed amount of parameters, still benefit from additional compute? The standard RL framework does not provide a language to answer these questions formally. Empirically, deep RL policies are often parameterized as neural networks with static architectures, conflating the amount of compute and the number of parameters. In this paper, we formalize compute bounded policies and prove that policies which use more compute can s
This paper addresses a fundamental question in AI development regarding the relationship between computational resources and learning efficacy, becoming increasingly relevant as compute becomes a critical bottleneck.
Understanding how compute affects RL policies independently of parameters offers critical insights for designing more efficient and powerful AI systems, potentially redefining the scaling laws of AI.
The formalization of compute-bounded policies could lead to a paradigm shift in how AI models are designed and optimized, moving beyond static architectures towards dynamic compute allocation.
- · Cloud computing providers
- · AI hardware manufacturers
- · Researchers optimizing RL algorithms
- · Organizations with significant compute resources
- · Companies with limited compute budgets relying on parameter-heavy models
- · Legacy AI design methodologies
Increased investment in computational infrastructure and more efficient AI architectures.
AI agents become more performant and adaptable given dynamic compute allocation, accelerating their deployment across industries.
The competitive landscape in AI shifts towards those who can efficiently manage and scale compute, potentially leading to greater concentration of advanced AI capabilities.
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Read at arXiv cs.LG