New research: Understanding how different LLMs approach reasoning through "thought anchors"
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New research: Understanding how different LLMs approach reasoning through "thought anchors"
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I just published a comparative study analyzing the reasoning patterns of Qwen3-0.6B vs DeepSeek-R1-Distill-1.5B using thought anchors - critical sentences that significantly impact task success probability.
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I just published a comparative study analyzing the reasoning patterns of Qwen3-0.6B vs DeepSeek-R1-Distill-1.5B using thought anchors - critical sentences that significantly impact task success probability.
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Key findings:
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Key findings:
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- DeepSeek-R1: Uses concentrated reasoning with fewer, high-impact steps (0.408 avg impact)
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- DeepSeek-R1: Uses concentrated reasoning with fewer, high-impact steps (0.408 avg impact)
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- Qwen3: Employs distributed reasoning spreading impact across multiple steps (0.278 avg impact)
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- Qwen3: Employs distributed reasoning spreading impact across multiple steps (0.278 avg impact)
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- Different risk-reward profiles: DeepSeek more consistent (82.7% positive steps), Qwen3 more exploratory (71.6% positive)
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- Different risk-reward profiles: DeepSeek more consistent (82.7% positive steps), Qwen3 more exploratory (71.6% positive)
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This reveals different cognitive architectures rather than simple performance differences. The models optimize for different reasoning strategies - consistency vs exploration.
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This reveals different cognitive architectures rather than simple performance differences. The models optimize for different reasoning strategies - consistency vs exploration.
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Both datasets are now available on HF:
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Both datasets are now available on HF:
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- Qwen3 thought anchors:  
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- Qwen3 thought anchors:  
https://huggingface.co/datasets/codelion/Qwen3-0.6B-pts-thought-anchors
codelion/Qwen3-0.6B-pts-thought-anchors
dataset
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https://huggingface.co/datasets/codelion/Qwen3-0.6B-pts-thought-anchors
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- DeepSeek-R1 thought anchors:  
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- DeepSeek-R1 thought anchors:  
https://huggingface.co/datasets/codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts-thought-anchors
codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts-thought-anchors
dataset
resource
https://huggingface.co/datasets/codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts-thought-anchors
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Built using our open-source PTS library for mechanistic interpretability analysis. All methodology is fully reproducible.
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Built using our open-source PTS library for mechanistic interpretability analysis. All methodology is fully reproducible.
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Full article: 
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Full article: 
https://huggingface.co/blog/codelion/understanding-model-reasoning-thought-anchors
https://huggingface.co/blog/codelion/understanding-model-reasoning-thought-anchors
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What reasoning patterns have you noticed in your model experiments? Would love to hear about other architectures showing similar cognitive diversity!
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What reasoning patterns have you noticed in your model experiments? Would love to hear about other architectures showing similar cognitive diversity!
New research: Understanding how different LLMs approach reasoning through "thought anchors"

I just published a comparative study analyzing the reasoning patterns of Qwen3-0.6B vs DeepSeek-R1-Distill-1.5B using thought anchors - critical sentences that significantly impact task success probability.

Key findings:
- DeepSeek-R1: Uses concentrated reasoning with fewer, high-impact steps (0.408 avg impact)
- Qwen3: Employs distributed reasoning spreading impact across multiple steps (0.278 avg impact)
- Different risk-reward profiles: DeepSeek more consistent (82.7% positive steps), Qwen3 more exploratory (71.6% positive)

This reveals different cognitive architectures rather than simple performance differences. The models optimize for different reasoning strategies - consistency vs exploration.

Both datasets are now available on HF:
- Qwen3 thought anchors:  https://huggingface.co/datasets/codelion/Qwen3-0.6B-pts-thought-anchors
- DeepSeek-R1 thought anchors:  https://huggingface.co/datasets/codelion/DeepSeek-R1-Distill-Qwen-1.5B-pts-thought-anchors

Built using our open-source PTS library for mechanistic interpretability analysis. All methodology is fully reproducible.

Full article: https://huggingface.co/blog/codelion/understanding-model-reasoning-thought-anchors

What reasoning patterns have you noticed in your model experiments? Would love to hear about other architectures showing similar cognitive diversity!
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John6666 codelion Abhaykoul
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