New SCALE framework enhances long-horizon language agent performance

Researchers have developed a new reinforcement learning framework called SCALE, designed to improve the performance of long-horizon language agents. SCALE addresses the challenge of assigning credit to intermediate actions when feedback is sparse or only available at the end of a task. By augmenting conventional return estimation with explicit prediction of eventual task success, SCALE aims to provide more reliable action credit assignment. Experiments across multiple benchmarks and model backbones show that SCALE improves task performance and critic estimation stability compared to existing RL baselines. AI

IMPACT This framework could lead to more capable and reliable AI agents for complex, long-term tasks.

RANK_REASON The cluster contains a research paper detailing a new framework for language agents. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
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• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• Proximal Policy Optimization
• SCALE
• ScienceCast
• Success-GAE

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