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-# Welcome to SynthLabs 👋
+SynthLabs
+A post-training AI research lab advancing and scaling synthetic reasoning
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+Welcome to the official GitHub for [SynthLabs.ai](https://www.synthlabs.ai/) 👋
-Welcome to the official GitHub for [SynthLabs.ai](https://www.synthlabs.ai/)
+---
## 🔬 Featured Research
+
+### [Generative Reward Models](https://www.synthlabs.ai/research/generative-reward-models)
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+Our latest work introduces Generative Reward Models (GenRM) and Chain-of-Thought GenRM (CoT-GenRM), a framework for preference learning that unifies RLHF and RLAIF approaches. We demonstrate that by combining iterative preference learning algorithms (STaR-DPO) with CoT-GenRM, we can train models that achieve comparable performance on in-domain data to Bradley-Terry Reward Models (currently best-in-class method), while vastly outperforming them on out-of-domain data (up to 45\% improvement). All while providing rationales for the model's predicted preference. The GenRM framework unifies language models and reward models under a single next-token prediction framing, reducing the infrastructure overhead required. The development of CoT-GenRM and STaR-DPO opens up new possibilities for AI alignment:
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+- **More Robust AI Systems**: Create AI systems that better generalize to new situations and maintain alignment with human values.
+- **Efficient Scaling**: Allow for more rapid iteration and refinement of AI behavior.
+- **Potential for Personalization**: Address the challenge of aligning AI with diverse and potentially conflicting human views.
+- **Improved Reasoning Capabilities**: Pave the way for AI systems that can continually improve their own reasoning and decision-making processes.
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+Contributions from Dakota Mahan\*, Duy Van Phung\*, Rafael Rafailov\*, Chase Blagden, Nathan Lile, Louis Castricato, Jan-Philipp Fränken, Chelsea Finn, and Alon Albalak\*.
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+**Learn more:**
+- [Blog](https://www.synthlabs.ai/research/generative-reward-models)
+- [ArXiV](https://arxiv.org/abs/2410.12832)
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+---
+
+### [PERSONA: A Reproducible Testbed for Pluralistic Alignment](https://www.synthlabs.ai/research/persona)
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+This work introduces PERSONA, a framework for evaluating the ability of language models to align with a diverse set of user values, using 1,586 synthetic personas, 3,868 prompts, and 317,200 preference pairs. We focus on pluralistic alignment because we want langauge models that can reflect a diverse set of values, not just the majority opinion, and we don't prescribe to a one-size-fits-all approach. PERSONA is synthetically constructed from U.S. census data, allowing us to generate a large, diverse dataset while ensuring privacy and reproducibility. The dataset and evaluation framework can be used for a variety of purposes, inlcluding: (1) a test bed, (2) a development environment, (3) a reproducible evaluation for pluralistic alignment approaches, (4) the personalization of language models, (5) and for preference elicitation.
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+Contributions from Louis Castricato*, Nathan Lile*, Rafael Rafailov, Jan-Philipp Fränken, and Chelsea Finn.
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+**Learn more:**
+- [Blog](https://www.synthlabs.ai/research/persona)
+- [ArXiv](https://arxiv.org/abs/2407.17387)
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+---
+
### [Suppressing Pink Elephants with Direct Principle Feedback](https://arxiv.org/abs/2402.07896)
-Our most recent work represents a significant advancement in the field of controllable language models. This [research addresses](https://arxiv.org/pdf/2402.07896.pdf) the 'Pink Elephant Problem' - instructing language models to avoid certain topics ("Pink Elephants") and focus on preferred ones ("Grey Elephants"). Key highlights:
+This work represents a significant advancement in the field of controllable language models. This [research addresses](https://arxiv.org/pdf/2402.07896.pdf) the 'Pink Elephant Problem' - instructing language models to avoid certain topics ("Pink Elephants") and focus on preferred ones ("Grey Elephants"). Key highlights:
- **Controllable Generation**: Dynamically adjust language models at inference time for diverse needs across multiple contexts
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- **Significant Performance Improvements**: After fine-tuning with DPF on our synthetic Pink Elephants dataset, our 13B fine-tuned LLaMA 2 model outperformed existing models and matched the performance of GPT-4 on our curated test set for the Pink Elephant Problem.
-Contributions from Louis Castricato, Nathan Lile, Suraj Anand, Hailey Schoelkopf, Siddharth Verma, and Stella Biderman. Read the full paper on [arXiv](https://arxiv.org/abs/2402.07896).
+Contributions from Louis Castricato, Nathan Lile, Suraj Anand, Hailey Schoelkopf, Siddharth Verma, and Stella Biderman.
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+**Learn more:**
+- [ArXiv](https://arxiv.org/abs/2402.07896)
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+---
## 📰 Featured Media/Press
- [Interviewing Louis Castricato on RLHF, Synth Labs, and the Future of Alignment](https://www.interconnects.ai/p/rlhf-interview-1-louis)
- [New Microsoft-Backed Startup Wants to Make AI Work As Intended](https://archive.is/vczUI)
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## 💼 Join Our Team
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-[](https://twitter.com/intent/follow?screen_name=synth_labs)
-[](https://discord.gg/46uN42SE6x)
-[](https://www.linkedin.com/company/synthlabsai)
-[](https://www.synthlabs.ai/)
-[](https://github.com/SynthLabsAI)
-[](https://huggingface.co/SynthLabsAI)
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Join us in shaping an aligned and impactful AI future! 🤝