Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!
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Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!
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Instead of using fixed thinking budgets, AutoThink:
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Instead of using fixed thinking budgets, AutoThink:
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- Classifies query complexity (HIGH/LOW) using adaptive classification
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- Classifies query complexity (HIGH/LOW) using adaptive classification
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- Dynamically allocates thinking tokens based on complexity
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- Dynamically allocates thinking tokens based on complexity
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- Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns
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- Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns
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Results on DeepSeek-R1-Distill-Qwen-1.5B:
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Results on DeepSeek-R1-Distill-Qwen-1.5B:
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- GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
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- GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
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- MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
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- MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
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- Uses fewer tokens than baseline approaches
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- Uses fewer tokens than baseline approaches
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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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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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Paper: AutoThink: efficient inference for reasoning LLMs
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Paper: AutoThink: efficient inference for reasoning LLMs
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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327
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Code and examples:
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Code and examples:
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https://github.com/codelion/optillm/tree/main/optillm/autothink
https://github.com/codelion/optillm/tree/main/optillm/autothink
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PTS implementation and technical details:
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PTS implementation and technical details:
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https://github.com/codelion/pts
https://github.com/codelion/pts
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https://huggingface.co/blog/codelion/pts
https://huggingface.co/blog/codelion/pts
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Adaptive classifier framework:
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Adaptive classifier framework:
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https://github.com/codelion/adaptive-classifier
https://github.com/codelion/adaptive-classifier
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Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
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Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
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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codelion John6666
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Asankhaya Sharma
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