content.raw: this paper lists ways to make reasoning LLMs more efficient:
content.type: text
content.value: this paper lists ways to make reasoning LLMs more efficient:
content.type: new_line
content.raw: > enforce token limits per reasoning step 
content.type: text
content.value: > enforce token limits per reasoning step 
content.type: new_line
content.raw: > route tasks to different models (small/large) 
content.type: text
content.value: > route tasks to different models (small/large) 
content.type: new_line
content.raw: > compress reasoning chains during SFT 
content.type: text
content.value: > compress reasoning chains during SFT 
content.type: new_line
content.raw: > reward based on reasoning length
content.type: text
content.value: > reward based on reasoning length
content.type: new_line
content.raw: > parallel search at test-time
content.type: text
content.value: > parallel search at test-time
content.type: new_line
content.raw: and more... 
content.type: text
content.value: and more... 
content.type: new_line
content.raw: @Xiaoye08
content.type: mention
content.user: Xiaoye08
content.type: text
content.raw: @yaful
content.type: mention
content.user: yaful
content.type: text
content.raw: @Warrieryes
content.type: mention
content.user: Warrieryes
content.type: new_line
content.label: A Survey of Efficient Reasoning for Large Reasoning Models: Language,
    Multimodality, and Beyond (2503.21614)
content.raw: https://huggingface.co/papers/2503.21614
content.id: 2503.21614
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2503.21614
rawContent: this paper lists ways to make reasoning LLMs more efficient:
  > enforce token limits per reasoning step 
  > route tasks to different models (small/large) 
  > compress reasoning chains during SFT 
  > reward based on reasoning length
  > parallel search at test-time
  and more... 
  @Xiaoye08 @yaful @Warrieryes
  https://huggingface.co/papers/2503.21614
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