Research Internship Reinforcement Learning (Summer)

Who are we

Our mission is to scale intelligence to serve humanity. Were 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 whats 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:

1. 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.
   

2. 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.

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.

• Develop and maintain codebases for both theoretical modeling and practical implementations.

• Collaborate with researchers to analyze results refine methodologies and prepare findings for publication.

• Contribute to the design of mechanisms for handling large rollouts such as summarization and hierarchical sub-agents.

• Document progress methodologies and outcomes clearly and comprehensively.

Requirements

• Technical Skills:

  • Strong background in machine learning particularly reinforcement learning and deep learning.

  • Proficiency in Python and experience with ML frameworks (e.g. PyTorch TensorFlow).

  • Familiarity with LLMs and their training paradigms.

  • Experience with coding tasks unit testing or compiler tools is a plus.

• Educational Background:

  • Currently pursuing a Masters or PhD in Computer Science Machine Learning or a related field.

• Soft Skills:

  • Ability to work independently and manage complex projects.

  • Strong problem-solving and analytical skills.

  • Excellent communication skills for collaborating with a research team.

• Additional:

  • Prior experience with RLVR self-distillation or large-scale ML experiments is highly desirable.

  • Willingness to learn and adapt to new methodologies and tools.

If some of the above doesnt line up perfectly with your experience we still encourage you to apply!

We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process please submit an Accommodations Request Form and we will work together to meet your needs.

Full-Time Employees at Cohere enjoy these Perks:

An open and inclusive culture and work environment

Work closely with a team on the cutting edge of AI research

Weekly lunch stipend in-office lunches & snacks

Full health and dental benefits including a separate budget to take care of your mental health

100% Parental Leave top-up for up to 6 months

Personal enrichment benefits towards arts and culture fitness and well-being quality time and workspace improvement

Remote-flexible offices in Toronto New York San Francisco London and Paris as well as a co-working stipend

6 weeks of vacation (30 working days!)

Required Experience:

Intern