arXiv:2607.07435v1 Announce Type: new Abstract: Agents acting on our behalf in the real world (e.g. placing phone calls) must learn online from costly, often irreversible interactions rather than cheap simulator steps. Two things follow. First, deployability depends on the path, not only the outcome. An agent must respect outcome-neutral constraints such as not repeatedly calling an unresponsive user, respecting business hours, or completing required authentication constraints that outcome-based rewards cannot express, since violating them frequently improves apparent success. Second, because
The increasing deployment of AI agents in real-world, high-stakes environments necessitates more sophisticated reward mechanisms that account for procedural correctness and safety, not just outcomes.
Achieving deployable real-world AI agents hinges on their ability to navigate complex environments safely and adhere to subtle constraints, which current outcome-based rewards often fail to capture.
This paper proposes a new method, RLVP, for penalizing undesirable paths while rewarding successful outcomes, shifting agent training methodologies towards greater safety and deployability in production systems.
- · AI agent developers
- · High-stakes industries (e.g., finance, healthcare)
- · AI ethics and safety researchers
- · AI models without nuanced reward functions
- · Companies with naive AI deployment strategies
AI agents become more robust and trustworthy in real-world applications by learning to respect operational constraints.
Increased adoption of AI agents in sensitive domains as their behavior becomes more predictable and safe.
New regulatory frameworks for AI agents may incorporate path-based evaluations, rather than solely outcome-based metrics, to ensure responsible deployment.
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Read at arXiv cs.LG