Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 27·8 min read
Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
TL;DR
Large language model agents perform well in controlled benchmarks but fail in real-world environments with noise and uncertainty. Researchers identified a mismatch between idealized training conditions and stochastic real-world dynamics.
✦ Why It Matters
Train LLM agents with environmental noise to prevent real-world performance collapse during deployment.
Key Takeaways
- Incorporate user and tool noise in training to improve agent performance.
- Progressively increase noise complexity to enhance adaptability.
- Test agents in dynamic environments to validate robustness.
- Utilize findings to bridge training and real-world application gaps.
How It Works
NoisyAgent modifies the training process by introducing user and tool noise, simulating real-world imperfections. User noise captures variability in how users interact with the agent, while tool noise reflects potential failures in the tools the agent uses.
By applying noise to only a subset of training rollouts and gradually increasing its intensity, agents learn to handle imperfections effectively, leading to improved decision-making in unpredictable environments.