---
format: "aidr-story-markdown/v1"
id: "0ce38ae102587f6e801b5864a1f1e0828cb4fdc2fb16602a1179ca62a227a497"
canonical_url: "https://aidr.today/0ce38ae1?lang=en"
title: "Incentivizing Self-Learning in Large Language Models via Reinforcement Learning"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-11-12T00:00:00.000Z"
category: "Research"
topics: ["llm"]
source_urls: ["https://huggingface.co/papers/2610.39671"]
summary: "Researchers introduced a BookNavigator interface and reinforcement learning method to incentivize self-learning in large language models, enabling them to autonomously retrieve, study, and apply knowledge from long-form reference materials, raising Intern-S2-Preview-35B's proof accuracy from 51.5% to 79.5% on a textbook-based mathematical problem-solving task."
---

# Incentivizing Self\-Learning in Large Language Models via Reinforcement Learning

> [Open the canonical story](<https://aidr.today/0ce38ae1?lang=en>)

**Published:** 2026-11-12T00:00:00.000Z
**Category:** Research
**Topics:** llm

## Summary

Researchers introduced a BookNavigator interface and reinforcement learning method to incentivize self\-learning in large language models, enabling them to autonomously retrieve, study, and apply knowledge from long\-form reference materials, raising Intern\-S2\-Preview\-35B's proof accuracy from 51\.5% to 79\.5% on a textbook\-based mathematical problem\-solving task\.

## Sources

- [Story source](<https://huggingface.co/papers/2610.39671>)