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Found 8 papers, 2 papers with code
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information.
Ranked #1 on
Question Answering
on Bamboogle
Hierarchical reinforcement learning with natural language subgoals
Hierarchical reinforcement learning has been a compelling approach for achieving goal directed behavior over long sequences of actions.
Melting Pot 2.0
Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios.
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.
TinyFedTL: Federated Transfer Learning on Tiny Devices
TinyML has rose to popularity in an era where data is everywhere.
FedFMC: Sequential Efficient Federated Learning on Non-iid Data
These experiments show that FedFMC substantially improves upon earlier approaches to non-iid data in the federated learning context without using a globally shared subset of data nor increase communication costs.
FedCD: Improving Performance in non-IID Federated Learning
Federated learning has been widely applied to enable decentralized devices, which each have their own local data, to learn a shared model.
