arXiv:2607.02288v1 Announce Type: new Abstract: While pessimism counteracts overestimation bias in offline reinforcement learning (RL), being overly conservative has been associated with hindering certain forms of generalization. However, in this paper we demonstrate that being overly pessimistic does not inherently prevent optimal generalization in contextual MDPs (CMDPs). Instead, we argue successful generalization depends not on the amount of pessimism, but whether the pessimistic structure respects the underlying symmetries of the optimal solution. We prove that a mildly pessimistic, non-s
The paper addresses a core challenge in offline reinforcement learning, a field gaining prominence as real-world data collection becomes more constrained and costly.
This research provides a fundamental insight into overcoming a key limitation in offline RL, potentially making agentic systems more robust and widely applicable in environments where experimentation is difficult.
The understanding shifts from merely reducing pessimism to structuring it intelligently, which could accelerate the development of more effective and generalizable offline RL algorithms.
- · AI algorithm developers
- · Companies using offline RL in production
- · Autonomous agents research
- · Methods that rely solely on scaling pessimism
Improved performance and reliability of AI agents trained on static datasets.
Faster deployment of AI agents in sensitive domains like finance or healthcare where real-world experimentation is risky.
Enhanced overall capability of AI agents to operate effectively in complex, unseen environments, accelerating the adoption of agentic systems.
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Read at arXiv cs.LG