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Case Western Reserve University
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Test-time scaling can significantly enhance LLM reasoning capabilities, but without clear protocols, results are often incomparable and misleading.
Token-level detection reveals the specific contributions of LLMs in collaborative writing, outperforming traditional document-level methods.
Single-rollout RL can rival multi-rollout performance for LLM reasoning, thanks to a new batchwise advantage estimation technique that dramatically improves value function accuracy.
Reasoning beats scale: a 1.5B parameter model, READER, outperforms models 100-1000x larger in detecting AI-generated text by explicitly generating a rationale for its decision.
