Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
Large language models can be trained through reinforcement learning to develop a meta-capability enabling continuous learning and adaptation across long sequences of tasks in dynam…
Hugging Face · Daily Papers
·Yanxi Chen, Weijie Shi
·
·▲ 5 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Yanxi Chen, Weijie Shi, Yuexiang Xie, Boyi Hu, Yaliang Li, Bolin Ding
- 5 upvotes da comunidade
- Temas: large language models, reinforcement learning, long rollout sequences, solve-task episodes, update-context episodes, end-to-end reinforcement learning
Resumo
Resumo original (em inglês), extraído do paper:
Large language models can be trained through reinforcement learning to develop a meta-capability enabling continuous learning and adaptation across long sequences of tasks in dynamic environments.
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