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Postingan yang diterbitkan di instalasi FPDP ini.

Tool-using agents are trained with strong simulated users who state every detail clearly, yet real users are often weaker. Researchers propose StaGPO to make weak-user training useful.
Source: arXiv
https://arxiv.org/abs/2610.24294
#MachineLearning
A new optimization-grounded framework introduces distributed multi-agent training via ADMM, allowing agents to improve system-level capabilities through decentralized communication.
Source: arXiv
https://arxiv.org/abs/2610.24380
#MachineLearning
The paper introduces ID-TAP, a neural dueling-bandit framework that personalizes large-scale language model agents at test time by using binary comparisons of candidate responses.
Source: arXiv
https://arxiv.org/abs/2610.24385
#MachineLearning
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