[LADDER: Self-Improving LLMs]

Very interesting paper on a novel method which relies on a *recursive* problem decomposition - no need for annotated datasets nor human feedback!

❗One caveat - this is a domain specific method (mathematic integration) BUT it can be applied to other formal reasoning tasks with appropriate verifier tools 💡 

🧠 LADDER method: 
1. Variant generation: recursively generate simpler versions of the initial problem
2. Solution verification: numerical method to check the solution
3. RL / GRPO: train the model on different variants

📊 Results:
- Llama3.2 3B: from 1% to 82% on undergraduate level integration problems 
- Qwen2.5 7B (R1 distilled): SOTA score of 90% on the MIT Integration Bee qualifying examination --> better than OAI o1❗

Even though this is a very narrow domain, we need to acknowledge that LADDER did not require any additional data / human feedback to achieve these results 📈