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What it takes to be a Cohere Labs Research Scholar

[HPP] Sara HookerAugust 10, 202510 min
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About the Cohere Labs Scholars Program

  • 💡 Purpose: Provides early career researchers with access to top-tier AI research at a frontier lab.
  • 🎯 Desired Qualities: Seeks individuals who are curious, passionate about learning, and open-minded, even with little prior research background.

Traits of Successful Researchers

  • 🧠 Critical Thinking: Ability to ask the right questions that challenge the status quo and spur innovation.
  • 🤝 Collaboration: Strong capacity for collaboration and synthesizing diverse viewpoints to achieve breakthroughs.
  • 🚀 Methodology: Requires discipline in methodology, clear hypotheses, and the ability to fail fast and iterate.

Scholar Experience and Research Focus

  • Supportive Environment: Scholars find the entire lab invested in their success, offering broad feedback beyond direct collaborators.
  • 🌐 Multilingual Alignment: Research focuses on making reinforcement learning from human feedback (RLHF) effective for multilingual foundation models, addressing the exclusion of non-English speakers.
  • ⚖️ Complex Preferences: This work is crucial for steering models towards a more realistic understanding of diverse human preferences, moving beyond a single global preference.

Advice for Aspiring Researchers

  • 🌱 Learning from Failure: Emphasizes viewing failures as learning opportunities and a path to growth.
  • 🛠️ Building Craft: Success involves perseverance and building a "craft" through repetition, which is foundational for developing research taste and perspective.

Importance of Open Science & Publishing

  • 📈 Industry Trend: Notes a trend of less publishing in industry labs due to competition and financial outcomes.
  • Cohere's Commitment: Cohere prioritizes publishing research and creating entry points for junior researchers to maintain the pace of AI innovation and foster better research.
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What’s Discussed

Cohere Labs Scholars ProgramAI researchEarly career researchersCuriosityCollaborationReinforcement learning from human feedback (RLHF)Preference trainingFoundation modelsMultilingual settingsModel alignmentOpen sciencePublishing researchIndustry labsPerseveranceLearning from failure
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