Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling
arXiv:2606.03102v1 Announce Type: new Abstract: Test-time scaling improves the reasoning performance of large language models but incurs substantial cost in both total computation and latency. Existing adaptive sampling methods partially mitigate this issue by dynamically deciding when to stop sampling, yet they typically rely on heuristic rules or rely on distribution assumptions. In this work, we formulate adaptive sampling as a Markov decision process (MDP). We train a lightweight sampling controller with reinforcement learning (RL) to jointly balance answer correctness, latency, and comput
The increasing computational cost and latency associated with large language models, particularly at test-time scaling, is driving research into more efficient inference methods.
This development addresses a critical bottleneck in the practical deployment and accessibility of advanced AI models, potentially leading to widespread adoption of more powerful LLMs.
The efficiency and cost-effectiveness of large language model inference will improve, making sophisticated AI reasoning more broadly available and enabling new applications.
- · AI developers
- · Cloud providers
- · Companies utilizing LLMs
- · Companies with inefficient AI inference solutions
Reduced operational costs and latency for large language model applications.
Democratization of advanced AI capabilities leading to a broader array of AI-powered products and services.
Increased competition among AI service providers as efficiency gains become a key differentiator.
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