arXiv:2605.30976v1 Announce Type: cross Abstract: We study stochastic linear bandits under a natural combination of batching and communication constraints: the time horizon is partitioned into batches of equal size $B$, and during each batch the learner sends $B$ requested arm pulls to an agent, who then observes the corresponding $B$ rewards and responds with a single bit of feedback to the learner. For each batch, the learner specifies the 1-bit quantization rule the agent uses, which may depend on all previously received bits but not on any past rewards directly. This setting addresses a si
This research addresses fundamental challenges in AI/ML performance under resource constraints, which is increasingly relevant as AI deployment scales and decentralizes.
Improved efficiency in communication-constrained AI systems can unlock new applications in edge computing, distributed learning, and environments with limited bandwidth or computational resources.
The ability to perform effective learning with 1-bit communication feedback per batch enhances the feasibility and robustness of decentralized AI systems and agent interactions.
- · Edge AI developers
- · Distributed computing platforms
- · Robotics with limited communication
- · AI systems requiring high bandwidth
More efficient and resilient AI models can be deployed in resource-constrained environments.
This efficiency could accelerate the development of autonomous agents that operate with minimal network overhead.
Reduced communication requirements might democratize access to advanced AI functionalities in remote or underdeveloped areas.
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