arXiv:2607.00634v1 Announce Type: new Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target objective directly, inducing an abrupt change from the source training objective. This abrupt transition can distort learned representations, including features that may still be useful for the new task. We investigate whether a more gradual transition can improve adaptation. We propose loss smoothing, a simple approach that interpolates between the source and target trai
The continuous deployment and adaptation of AI models under varying real-world conditions necessitates more robust and stable learning methodologies.
Improving adaptation techniques for AI models directly enhances their reliability and performance in dynamic environments, critical for widespread AI adoption across industries.
This research suggests a more stable and effective method for fine-tuning and adapting AI models, potentially leading to faster and more reliable deployment cycles.
- · AI developers
- · Companies deploying AI in dynamic settings
- · Reinforcement learning researchers
- · Traditional abrupt fine-tuning methods
- · Systems highly sensitive to distribution shifts
AI models will become more resilient to changes in their operating environment, reducing the need for costly re-training.
This could accelerate the development of autonomous AI agents capable of continuous, stable learning in the wild.
More robust adaptation could lead to faster commercialization of complex AI systems in areas like robotics and real-time decision-making.
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Read at arXiv cs.LG