arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks without any parameter updates, instead relying on an expanding context of interaction history. While ICRL has shown impressive generalization, safety during this adaptation process remains unexplored, limiting its applicability in real-world deployments where test-time behavior is expected to be safe. In this work, we propose SCARED: Safe Contextual Adaptive Reinforcement via Exact-penalty Dual, th
The rapid advancement and deployment of in-context reinforcement learning necessitates immediate attention to safety protocols to enable its real-world applicability.
This work addresses a critical limitation of powerful AI systems, enabling safer deployment in sensitive or high-stakes environments, reducing risks associated with autonomous decision-making.
The focus on ensuring safety during AI adaptation to new tasks, without requiring continuous retraining, shifts the paradigm towards more robust and trustworthy autonomous agents.
- · AI developers
- · Industries deploying AI agents
- · Regulators
- · Developers ignoring safety-by-design
- · Sectors reliant on unconstrained AI deployment
Wider adoption of in-context reinforcement learning in critical applications becomes feasible.
Increased trust in AI systems could accelerate automation across various sectors, impacting labor markets.
The definition of 'safe' AI could become a key competitive differentiator and regulatory battleground.
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