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Found 4 papers, 0 papers with code
Predicting the Performance of Foundation Models via Agreement-on-the-Line
Recently, it was shown that ensembles of neural networks observe the phenomena ``agreement-on-the-line'', which can be leveraged to reliably predict OOD performance without labels.
Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method
This paper presents a novel reinforcement learning (RL) approach called HAAM-RL (Heuristic Algorithm-based Action Masking Reinforcement Learning) for optimizing the color batching re-sequencing problem in automobile painting processes.
Jailbreaking is Best Solved by Definition
The rise of "jailbreak" attacks on language models has led to a flurry of defenses aimed at preventing the output of undesirable responses.
Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas.
