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Getting Started with RL + LLM Research

Welcome! This guide helps you navigate the repository based on your goals and background.

🎯 Choose Your Path

I'm New to Reinforcement Learning

Start here → Archive/2017-Course-Notes

Learning sequence:

  1. Watch intro talks (main README)
  2. Read Sutton & Barto book chapters
  3. Follow David Silver's course
  4. Study CS294 notes
  5. Implement classic algorithms (DQN, A3C, PPO)

Time estimate: 1-2 months for foundations

I Know RL, New to LLMs + RL

Start here → Modern-RL-Research/RLHF-and-Alignment

Learning sequence:

  1. Understand RLHF basics (PPO, reward models)
  2. Learn about DPO as simpler alternative
  3. Study safety considerations
  4. Explore code generation applications
  5. Review recent papers (PAPERS.md files)

Time estimate: 2-3 weeks to get up to speed

I Want to Build/Research Code Generation

Start here → Modern-RL-Research/LLM-Code-Generation

Learning sequence:

  1. Study AlphaCode and CodeRL papers
  2. Understand execution feedback as rewards
  3. Learn about safety and sandboxing
  4. Review benchmarks (HumanEval, MBPP)
  5. Experiment with TRL library
  6. Check PAPERS.md for latest research

Time estimate: 1-2 weeks for overview, ongoing for deep work

I'm Researching Program Synthesis

Start here → Modern-RL-Research/LLM-RL-Program-Synthesis

Key papers to read:

  1. AlphaCode (Science 2022) - Foundation paper
  2. CodeRL (NeurIPS 2022) - RL framework
  3. Process-supervised RL (2025) - Recent advances
  4. Browse PAPERS.md for latest work

Also check:

  • Berkeley's safe execution work
  • Test-time compute scaling (o1, DeepSeek R1)

📚 Repository Structure Quick Reference

Study-Reinforcement-Learning/
│
├── Archive/                          # Classic RL (2017)
│   └── 2017-Course-Notes/
│       ├── CS294-DeepRL-Berkeley/    # Levine, Schulman, Finn
│       └── Elements-Of-RL/           # Sutton & Barto concepts
│
├── Modern-RL-Research/               # Cutting-edge (2022-2025)
│   ├── LLM-RL-Program-Synthesis/    # AlphaCode, competitive coding
│   │   ├── README.md
│   │   └── PAPERS.md                # 50 recent papers
│   │
│   ├── LLM-Code-Generation/         # Practical code generation
│   │   ├── README.md
│   │   └── PAPERS.md                # 271 recent papers
│   │
│   └── RLHF-and-Alignment/          # PPO, DPO, GRPO
│       ├── README.md
│       └── PAPERS.md                # 111 recent papers
│
├── scripts/                          # Automation tools
│   ├── arxiv_paper_collector.py    # Auto-fetch papers
│   └── papers_database.json         # Complete paper database
│
└── readme.md                         # Main entry point

🔥 What's Hot Right Now (2025)

Top Research Areas

  1. Test-Time Compute Scaling - o1, DeepSeek R1 approaches
  2. DPO Variants - Simpler alternatives to PPO
  3. Safe Code Generation - Sandboxing, Constitutional AI
  4. Multi-Modal Code - From diagrams/sketches to code
  5. Formal Verification + RL - Provably correct code

Key Papers to Read (2024-2025)

  • 432 papers collected across all topics
  • See PAPERS.md in each Modern-RL-Research subdirectory
  • Organized by year (2025 → 2022)

🛠️ Hands-On Learning

Beginner Projects

  1. Implement Q-Learning on simple grid worlds
  2. Train DQN on Atari games
  3. Build Policy Gradient agent for CartPole

Intermediate Projects

  1. Fine-tune small LLM with RLHF on toy task
  2. Implement DPO and compare with PPO
  3. Create code completion model with execution feedback

Advanced Projects

  1. Reproduce CodeRL results on HumanEval
  2. Build safe code executor with sandboxing
  3. Experiment with test-time compute scaling

📖 Essential Reading by Topic

Classic RL

  • Sutton & Barto (2017) - The RL bible
  • Spinning Up in Deep RL (OpenAI) - Practical guide
  • CS294/285 lectures (Berkeley) - Academic depth

Modern RLHF

  • InstructGPT paper (2022) - Started the RLHF trend
  • DPO paper (2023) - Simpler alternative
  • PAPERS.md files - Latest research

Code Generation

  • AlphaCode (2022) - Breakthrough paper
  • CodeRL (2022) - Framework
  • Berkeley Safe Code (2025) - Safety focus

🔧 Tools & Frameworks

For Learning RL

  • Gymnasium (OpenAI Gym) - Standard environments
  • Stable-Baselines3 - Pre-built algorithms
  • RLlib - Scalable RL library

For RLHF

  • TRL (Transformers RL) - Hugging Face library
  • DeepSpeed - Efficient training
  • Composer - Mosaic ML framework

For Code Generation

  • HumanEval - Benchmark dataset
  • Judge0 / Sphere Engine - Code execution
  • Bandit / Semgrep - Security scanning

📊 Benchmarks to Track

Code Generation

  • HumanEval (164 problems) - Function-level
  • MBPP (1000 problems) - Basic Python
  • APPS (10K problems) - Competition-level
  • LiveCodeBench - Continuously updated
  • SWE-bench - Real GitHub issues

Reasoning

  • GSM8K - Grade school math
  • MATH - Competition mathematics
  • Big-Bench Hard - Challenging tasks

🎓 Recommended Learning Timeline

Month 1: Foundations

  • Week 1-2: Classic RL concepts, MDPs, value functions
  • Week 3-4: Policy gradients, actor-critic methods

Month 2: Deep RL

  • Week 1-2: DQN, A3C, PPO implementations
  • Week 3-4: Advanced topics (TRPO, SAC, TD3)

Month 3: LLMs + RL

  • Week 1-2: RLHF basics, reward modeling
  • Week 3-4: Code generation, program synthesis

Ongoing: Stay Current

  • Run arxiv_paper_collector.py monthly
  • Follow researchers on Twitter/X
  • Attend conference workshops
  • Join r/reinforcementlearning

💡 Tips for Success

Study Tips

  1. Implement, don't just read - Code algorithms from scratch
  2. Start simple - Master toy problems before complex tasks
  3. Join communities - Reddit, Discord, Twitter
  4. Read papers actively - Take notes, ask questions
  5. Reproduce results - Verify claims with your own experiments

Research Tips

  1. Focus on gaps - What problems remain unsolved?
  2. Build on existing work - Don't start from zero
  3. Collaborate - Find research groups, mentors
  4. Share findings - Blog posts, papers, code
  5. Stay updated - Use the arxiv script regularly

Practical Tips

  1. Use pretrained models - Don't train from scratch
  2. Start small - Scale up gradually
  3. Track experiments - Use Weights & Biases, MLflow
  4. Version control - Git for code, DVC for data
  5. Document everything - Future you will thank you

🤝 Contributing

Found something useful? Share it!

Ways to contribute:

  • Add papers to collections
  • Update scripts with new features
  • Write tutorials or guides
  • Fix errors or broken links
  • Share your projects

How to contribute:

  1. Fork the repository
  2. Make your changes
  3. Submit a pull request
  4. Discuss in issues

🔗 External Resources

Communities

Newsletters

  • The Batch (DeepLearning.AI) - Weekly ML news
  • ImportAI - AI research summaries
  • TLDR AI - Daily AI updates

Conferences

  • NeurIPS - December, largest ML conference
  • ICLR - May, representation learning focus
  • ICML - July, broad ML scope
  • EMNLP/ACL - NLP conferences with code papers

❓ Common Questions

Q: Should I learn classic RL first? A: Yes! Understanding MDPs, value functions, and policy gradients is essential before diving into LLM applications.

Q: Can I skip the math? A: Some math is necessary, but you can learn alongside practical implementation. Don't let math block your progress.

Q: What programming languages do I need? A: Python is essential. PyTorch or JAX for deep learning. Familiarity with Git and command line.

Q: How much compute do I need? A: For learning: Just a laptop. For research: GPU access helpful (Colab, cloud services). For production: Significant resources.

Q: Where do I find collaborators? A: Online communities, university research groups, open source projects, conference workshops.

📧 Staying Updated

This repository is actively maintained. To stay current:

  1. Star and watch this repo on GitHub
  2. Run the arxiv script monthly for new papers
  3. Check main README for announcements
  4. Follow the field via Twitter, Reddit, newsletters

🚀 Ready to Start?

Pick your path above and dive in! Remember:

  • Start small, build gradually
  • Implement what you learn
  • Share your journey
  • Ask questions
  • Have fun!

Good luck on your RL journey! 🎯

Last Updated: 2025