Research Internship Reinforcement Learning (Summer)

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Cohere · Paris, France

Full-timeRemote1mo ago30+ days old, may be filled

Deep LearningLLMsMachine LearningMovePythonPyTorch

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Responsibilities

Conduct literature reviews and implement state-of-the-art algorithms in RL and self-distillation.
Design and execute experiments to evaluate the effectiveness of proposed methods on code generation and agentic tasks.

Additional Information

Who are we? Our mission is to scale intelligence to serve humanity. We're training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. We like to work hard and move fast to do what's best for our customers. Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future! Duration: Minimum 4 months (summer 2026, with potential extension) About the Project This internship offers a unique opportunity to contribute to cutting-edge research in reinforcement learning (RL) and large language models (LLMs), focusing on two interconnected projects: Combining Self-Distillation and Reinforcement Learning for LLMs, with Applications to Code and Agentic Tasks This project explores how LLMs can improve through self-reflection and iterative learning by combining reinforcement learning with verifiable rewards (RLVR) and self-distillation. The focus is on scenarios where structured feedback from verifiers, compilers, unit tests, or tool calls enables models to detect errors, revise outputs, and learn from failures. The internship will bridge theoretical mathematical modeling of self-distillation with practical, production-oriented implementation. Dealing with Extremely Large Rollouts in RLVR As RLVR becomes a cornerstone for training reasoning-oriented LLMs, the challenge of handling extremely large rollouts grows. This project investigates mechanisms such as summarization, memory, context compaction, hierarchical sub-agents, and resumable rollouts to enable unbounded or very long trajectories. It also explores how to effectively learn from such trajectories, as traditional RLVR objectives fail when episodes exceed context window limits. Both projects are grounded in recent research and aim to advance the state-of-the-art in LLM training and deployment.

Research Internship Reinforcement Learning (Summer)

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