Reasoning Curriculum: Bootstrapping Broad LLM Reasoning from Math

TL;DR

This paper introduces a simple two-stage reinforcement learning curriculum that enhances large language models' reasoning abilities across multiple domains by first focusing on math and then transferring skills to other areas.

Contribution

It presents a minimal, backbone-agnostic curriculum that effectively improves reasoning in LLMs without requiring specialized reward models.

Findings

Consistent reasoning improvements across multiple models and domains

Both curriculum stages are necessary for optimal performance

Math-first approach enhances complex problem-solving skills

Abstract

Reinforcement learning (RL) can elicit strong reasoning in large language models (LLMs), yet most open efforts focus on math and code. We propose Reasoning Curriculum, a simple two-stage curriculum that first elicits reasoning skills in pretraining-aligned domains such as math, then adapts and refines these skills across other domains via joint RL. Stage 1 performs a brief cold start and then math-only RL with verifiable rewards to develop reasoning skills. Stage 2 runs joint RL on mixed-domain data to transfer and consolidate these skills. The curriculum is minimal and backbone-agnostic, requiring no specialized reward models beyond standard verifiability checks. Evaluated on Qwen3-4B and Llama-3.1-8B over a multi-domain suite, reasoning curriculum yields consistent gains. Ablations and a cognitive-skill analysis indicate that both stages are necessary and that math-first elicitation increases cognitive behaviors important for solving complex problems. Reasoning Curriculum provides a compact, easy-to-adopt recipe for general reasoning.