DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
arXiv preprint arXiv:2403.09032 , year=
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Early Stopping Rollout limits on-policy distillation to initial tokens to counter teacher decay, outperforming full rollouts in accuracy, efficiency, and stability while revealing cascading alignment effects.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
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Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling
DRATS derives a minimax objective from a feasibility formulation of MTRL to adaptively sample tasks with the largest return gaps, leading to better worst-task performance on MetaWorld benchmarks.
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Less is More: Early Stopping Rollout for On-Policy Distillation
Early Stopping Rollout limits on-policy distillation to initial tokens to counter teacher decay, outperforming full rollouts in accuracy, efficiency, and stability while revealing cascading alignment effects.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
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