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Scalable code generation and analysis RL environment scalable for large language models (LLMs) training Job Opening In Villeneuve-d'Ascq – Now Hiring INRIA

Scalable code generation and analysis RL environment scalable for large language models (LLMs) training

    France Jobs Expertini Expertini France Jobs Villeneuve D'ascq Computer Occupations Scalable Code Generation And Analysis Rl Environment Scalable For Large Language Models (Llms) Training

Job description

Contexte et atouts du poste

You will work inside the MAGNET team at Inria Lille


More particularly, on the Reasoning Core project 


Funded by ANR Adada, collaborating with the PI, a PhD student and an engineer

Mission confiée

The internship will focus on the design of a reinforcement learning environment for procedural task generation in code completion and understanding.

Unlike existing benchmarks that rely on static datasets, the objective is to create renewable, verifiable tasks with an adaptive difficulty knob and curriculum learning.

This will involve:


  • Studying existing environments for procedural reasoning and code generation, such as Reasoning Gym (Shen et al., Reasoning Gym: Procedurally Generated Reasoning Environments for Pretraining and Evaluating LLMs, 2024), DELTA-Code (Sun et al., DELTA-Code: A Benchmark for RL-based Algorithm Learning in Code LLMs, ICLR 2025), StepCoder (Dou et al., StepCoder: Improving LLM-Based Code Generation via Step-wise RL, 2024), and RLEF (Gehring et al., Reinforcement Learning from Execution Feedback for Code Generation, NeurIPS 2024).




  • Identifying gaps in existing approaches, in particular the lack of procedurally generated static analysis tasks (e.g., call-graph construction, error detection, performance profiling).




  • Proposing new generators for Python code tasks that produce infinite, verifiable problem instances, with evaluation grounded in unit tests, execution traces, or static analyzers.


  • Principales activités

    Main activities


  • Implement procedural generators for code tasks (Python focus), inspired by TinyPy-style grammar synthesis (Naïr et al., Curriculum Learning for Code Execution with Synthetic Python Programs, 2023) and code perturbation methods




  • Integrate these generators in a Gym-style RL environment with automatic scoring and curriculum scheduling (easy → hard).




  • Explore a novel task family (e.g., static code reasoning, code completion, debugging, refactoring, performance analysis, or multi-file repository puzzles) and evaluate whether LLMs can improve through curriculum-based RL.


  • Compétences

    Excellent python coding skill and code understanding


    Critical thinking and scientific culture


    Good vibe coding capabilities


    Some perfectionism, liking iteration and refinment


    Ability to process scientific literature

    Avantages

  • Subsidized meals

  • Partial reimbursement of public transport costs

  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)

  • Possibility of teleworking and flexible organization of working hours

  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)

  • Social, cultural and sports events and activities

  • Access to vocational training

  • Social security coverage
  • Rémunération

    internship allowance according to the amount in force

    Required Skill Profession

    Computer Occupations


    • Job Details

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