AI RESEARCH
An Empirical Investigation of Practical LLM-as-a-Judge Improvement Techniques on RewardBench 2
arXiv CS.CL
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ArXi:2604.13717v1 Announce Type: new LLM-as-a-judge, using a language model to score or rank candidate responses, is widely used as a scalable alternative to human evaluation in RLHF pipelines, benchmarking, and application layer evaluations (evals). However, judgment reliability depends heavily on prompting and aggregation strategy. We present an empirical investigation of practical, drop-in techniques that improve GPT-5.4 judge accuracy on RewardBench 2 without any finetuning.