Top-H decoding is a computationally efficient greedy algorithm for an entropy-constrained mass maximization problem that improves the creativity-coherence trade-off over min-p sampling in LLM text generation.
Gpqa: A graduate-level google-proof q&a benchmark
3 Pith papers cite this work. Polarity classification is still indexing.
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The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
A 14B reasoning model trained via supervised fine-tuning on selected prompts and o3-mini traces, plus outcome RL, outperforms larger open models like DeepSeek-R1-Distill-Llama-70B on math, coding, planning and related benchmarks.
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Phi-4-reasoning Technical Report
A 14B reasoning model trained via supervised fine-tuning on selected prompts and o3-mini traces, plus outcome RL, outperforms larger open models like DeepSeek-R1-Distill-Llama-70B on math, coding, planning and related benchmarks.