content.raw: Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!
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content.value: Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!
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content.raw: Instead of using fixed thinking budgets, AutoThink:
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content.value: Instead of using fixed thinking budgets, AutoThink:
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content.raw: - Classifies query complexity (HIGH/LOW) using adaptive classification
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content.value: - Classifies query complexity (HIGH/LOW) using adaptive classification
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content.raw: - Dynamically allocates thinking tokens based on complexity
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content.value: - Dynamically allocates thinking tokens based on complexity
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content.raw: - Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns
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content.value: - Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns
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content.raw: Results on DeepSeek-R1-Distill-Qwen-1.5B:
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content.value: Results on DeepSeek-R1-Distill-Qwen-1.5B:
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content.raw: - GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
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content.value: - GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
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content.raw: - MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
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content.value: - MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
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content.raw: - Uses fewer tokens than baseline approaches
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content.value: - Uses fewer tokens than baseline approaches
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content.raw: Works with any local reasoning model - DeepSeek, Qwen, Llama, custom models. The technique combines our research on Pivotal Token Search (PTS) implementation and adaptive classification frameworks.
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content.value: Works with any local reasoning model - DeepSeek, Qwen, Llama, custom models. The technique combines our research on Pivotal Token Search (PTS) implementation and adaptive classification frameworks.
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content.raw: Paper: AutoThink: efficient inference for reasoning LLMs
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content.value: Paper: AutoThink: efficient inference for reasoning LLMs
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content.href: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
content.raw: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
content.type: link
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content.raw: Code and examples:
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content.value: Code and examples:
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content.href: https://github.com/codelion/optillm/tree/main/optillm/autothink
content.raw: https://github.com/codelion/optillm/tree/main/optillm/autothink
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content.raw: PTS implementation and technical details:
content.type: text
content.value: PTS implementation and technical details:
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content.href: https://github.com/codelion/pts
content.raw: https://github.com/codelion/pts
content.type: link
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content.href: https://huggingface.co/blog/codelion/pts
content.raw: https://huggingface.co/blog/codelion/pts
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content.raw: Adaptive classifier framework:
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content.value: Adaptive classifier framework:
content.type: new_line
content.href: https://github.com/codelion/adaptive-classifier
content.raw: https://github.com/codelion/adaptive-classifier
content.type: link
content.type: new_line
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content.raw: Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
content.type: text
content.value: Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
rawContent: Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!
  
  Instead of using fixed thinking budgets, AutoThink:
  - Classifies query complexity (HIGH/LOW) using adaptive classification
  - Dynamically allocates thinking tokens based on complexity
  - Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns
  
  Results on DeepSeek-R1-Distill-Qwen-1.5B:
  - GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
  - MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
  - Uses fewer tokens than baseline approaches
  
  Works with any local reasoning model - DeepSeek, Qwen, Llama, custom models. The technique combines our research on Pivotal Token Search (PTS) implementation and adaptive classification frameworks.
  
  Paper: AutoThink: efficient inference for reasoning LLMs
  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
  
  Code and examples:
  https://github.com/codelion/optillm/tree/main/optillm/autothink
  
  PTS implementation and technical details:
  https://github.com/codelion/pts
  https://huggingface.co/blog/codelion/pts
  
  Adaptive classifier framework:
  https://github.com/codelion/adaptive-classifier
  
  Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
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commentators._id: 67ee7b241cd073e07c35a93e
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commentators.fullname: L
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commentators.fullname: Asankhaya Sharma
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