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Foundation models supply reinforcement learning (RL) with priors that mitigate its longstanding weaknesses in sample efficiency and transfer, but their token-by-token generation makes queries sequential and costly. Jev, a recently released decision model, generates nothing and returns calibrated, typed answers in a sin...
A novel MTRL framework called Task-Specific feature Selector and Scheduler (T3S) is proposed, which consists of two components: a feature selector and a task scheduler that consistently outperforms the state-of-the-art M TRL algorithms on various robotics manipulation tasks.
An offline model-based RL framework for cost-controllable sequential incentive allocation is developed and an independent counterfactual scorer evaluates each learned policy on held-out logs, enabling pre-launch selection without costly online exposure.
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