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Asankhaya Sharma

codelion
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http://asankhaya.github.io/

AI & ML interests

AI/ML, Dev Tools and Application Security

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replied to their post 2 days ago
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?
reacted to their post with ❤️ 3 days ago
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?
reacted to their post with 👀 3 days ago
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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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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AutoThink: Adaptive Reasoning for Large Language Models

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