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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: 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!
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: 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!