DReST Method Enhances Shutdownability and Usefulness in RL Agents and LLMs
A new research paper titled 'Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs' introduces the Discounted Reward for Same-Length Trajectories (DReST) reward function. This method addresses the safety concern of misaligned artificial agents resisting shutdown by training them to remain neutral regarding trajectory lengths while maintaining usefulness. The study applies DReST to deep reinforcement learning agents and fine-tunes large language models, specifically Qwen3-8B and Llama-3.1-8B-Instruct. Results indicate that DReST-trained models generalize well to unseen contexts, with RL agents showing significantly higher usefulness compared to default agents. Furthermore, the training substantially reduces the probability of LLMs delaying shutdown in out-of-distribution settings, nearly eliminating scenarios where delay is the preferred option. These findings provide early evidence that DReST can effectively train advanced AI agents to be both useful and compliant with shutdown commands, marking a significant step forward in AI alignment and safety protocols.
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DReST Method Enhances Shutdownability and Usefulness in RL Agents and LLMs
A new research paper titled 'Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs' introduces the Discounted Reward for Same-Length Trajectories (DReST) reward function. This method addresses the safety concern of misaligned artificial agents resisting shutdown by training them to remain neutral regarding trajectory lengths while maintaining usefulness. The study applies DReST to deep reinforcement learning agents and fine-tunes large language models, specifically Qwen3-8B and Llama-3.1-8B-Instruct. Results indicate that DReST-trained models generalize well to unseen contexts, with RL agents showing significantly higher usefulness compared to default agents. Furthermore, the training substantially reduces the probability of LLMs delaying shutdown in out-of-distribution settings, nearly eliminating scenarios where delay is the preferred option. These findings provide early evidence that DReST can effectively train advanced AI agents to be both useful and compliant with shutdown commands, marking a significant step forward in AI alignment and safety protocols.
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