Introducing Pivotal Token Search (PTS): A new technique for targeted LLM alignment
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Introducing Pivotal Token Search (PTS): A new technique for targeted LLM alignment
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Excited to share Pivotal Token Search (PTS), a technique for identifying and optimizing critical decision points in LLM generations! 
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Excited to share Pivotal Token Search (PTS), a technique for identifying and optimizing critical decision points in LLM generations! 
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GitHub repository: 
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GitHub repository: 
https://github.com/codelion/pts
https://github.com/codelion/pts
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What is PTS?
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What is PTS?
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PTS helps identify specific "pivotal tokens" that dramatically shift the probability of a successful generation. Unlike traditional DPO which treats all tokens equally, PTS focuses optimization on the tokens that actually matter for success.
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PTS helps identify specific "pivotal tokens" that dramatically shift the probability of a successful generation. Unlike traditional DPO which treats all tokens equally, PTS focuses optimization on the tokens that actually matter for success.
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Inspired by Microsoft's recent Phi-4 paper (which used this technique to achieve SOTA reasoning with only 14B parameters), PTS is especially effective for:
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Inspired by Microsoft's recent Phi-4 paper (which used this technique to achieve SOTA reasoning with only 14B parameters), PTS is especially effective for:
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- Mathematical reasoning
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- Mathematical reasoning
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- Coding tasks
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- Coding tasks
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- Multi-step problem solving
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- Multi-step problem solving
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- Any domain where specific decision points strongly impact outcomes
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- Any domain where specific decision points strongly impact outcomes
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What we're releasing today: 
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What we're releasing today: 
https://huggingface.co/collections/codelion/pivotal-token-search-68241145d8b8502122f3ce4f
codelion/pivotal-token-search-68241145d8b8502122f3ce4f
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https://huggingface.co/collections/codelion/pivotal-token-search-68241145d8b8502122f3ce4f
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1. Open-source code:
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1. Open-source code:
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- Complete implementation of the PTS algorithm
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- Complete implementation of the PTS algorithm
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- Data generation pipelines
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- Data generation pipelines
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- Usage examples and documentation
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- Usage examples and documentation
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2. Huggingface resources:
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2. Huggingface resources:
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- Datasets collection: 
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- Datasets collection: 
https://huggingface.co/datasets?other=pts
https://huggingface.co/datasets?other=pts
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  * Pre-generated preference pairs for various domains
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  * Pre-generated preference pairs for various domains
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  * Ready to use in your DPO training pipelines
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  * Ready to use in your DPO training pipelines
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- Models collection: 
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- Models collection: 
https://huggingface.co/models?other=pts
https://huggingface.co/models?other=pts
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  * Pre-trained models fine-tuned with PTS
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  * Pre-trained models fine-tuned with PTS
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  * Specialized versions for different reasoning tasks
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  * Specialized versions for different reasoning tasks
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The algorithm is straightforward to implement and can significantly improve your model's reasoning capabilities. Check out the repository for details on getting started!
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The algorithm is straightforward to implement and can significantly improve your model's reasoning capabilities. Check out the repository for details on getting started!
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We welcome feedback, contributions, and collaborations. Let us know if you use PTS in your projects!
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We welcome feedback, contributions, and collaborations. Let us know if you use PTS in your projects!
Introducing Pivotal Token Search (PTS): A new technique for targeted LLM alignment

Excited to share Pivotal Token Search (PTS), a technique for identifying and optimizing critical decision points in LLM generations! 

GitHub repository: https://github.com/codelion/pts

What is PTS?
PTS helps identify specific "pivotal tokens" that dramatically shift the probability of a successful generation. Unlike traditional DPO which treats all tokens equally, PTS focuses optimization on the tokens that actually matter for success.

Inspired by Microsoft's recent Phi-4 paper (which used this technique to achieve SOTA reasoning with only 14B parameters), PTS is especially effective for:
- Mathematical reasoning
- Coding tasks
- Multi-step problem solving
- Any domain where specific decision points strongly impact outcomes

What we're releasing today: https://huggingface.co/collections/codelion/pivotal-token-search-68241145d8b8502122f3ce4f

1. Open-source code:
- Complete implementation of the PTS algorithm
- Data generation pipelines
- Usage examples and documentation

2. Huggingface resources:
- Datasets collection: https://huggingface.co/datasets?other=pts
  * Pre-generated preference pairs for various domains
  * Ready to use in your DPO training pipelines

- Models collection: https://huggingface.co/models?other=pts
  * Pre-trained models fine-tuned with PTS
  * Specialized versions for different reasoning tasks

The algorithm is straightforward to implement and can significantly improve your model's reasoning capabilities. Check out the repository for details on getting started!

We welcome feedback, contributions, and collaborations. Let us know if you use PTS in your projects!
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