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Your AI models are failing in production—Here’s how to fix model selection
The Allen Institute of AI updated its reward model evaluation RewardBench to better reflect real-life scenarios for enterprises.
“As reward models became more advanced and use cases more nuanced, we quickly recognized with the community that the first version didn’t fully capture the complexity of real-world human preferences,” he said. For inference time scaling or data filtering, RewardBench 2 has shown that they can select the best model for their domain and see correlated performance,” Lambert said. Lambert noted that benchmarks like RewardBench offer users a way to evaluate the models they’re choosing based on the “dimensions that matter most to them, rather than relying on a narrow one-size-fits-all score.” He said the idea of performance, which many evaluation methods claim to assess, is very subjective because a good response from a model highly depends on the context and goals of the user.
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