Scaf-grpo Enhances LLM Reasoning, Overcoming Learning Cliffs with Scaffolded Policy Optimization

Enhancing the reasoning abilities of large language models remains a significant challenge, particularly when tackling complex problems that lie beyond their initial capabilities. Xichen Zhang from The Hong Kong University of Science and Technology, Sitong Wu, and Yinghao Zhu from The University of Hong Kong, alongside Haoru Tan, Shaozuo Yu, and Ziyi He, address this issue with a novel training framework called Scaf-GRPO, or Scaffolded Group Relative Policy Optimization. The team recognised that current reinforcement learning methods often fail when faced with difficult problems, as a lack of initial success prevents the model from learning effectively. Scaf-GRPO overcomes this “learning cliff” by strategically providing minimal guidance only when independent learning plateaus, offering tiered hints that range from abstract concepts to concrete steps. Through extensive testing on challenging mathematical benchmarks, the researchers demonstrate that Scaf-GRPO boosts performance, achieving a relative 44. 3% improvement in pass rate on the AIME24 benchmark, and represents a robust methodology for unlocking a language model’s ability to solve previously intractable problems.

Scaffolding Reinforcement Learning for Mathematical Reasoning

This research details Scaf-GRPO, a reinforcement learning framework designed to improve performance on complex reasoning tasks, specifically mathematical problem-solving. The core idea involves augmenting a standard GRPO algorithm with a scaffolding mechanism that provides minimal hints when the model encounters difficulties, preventing over-reliance on hints and ensuring scaffolding is reserved for genuinely challenging problems. Detailed analyses demonstrate the effectiveness of this exemption period and the overall framework in overcoming the “learning cliff,” where models struggle with complex tasks and fail to make progress.

Progressive Scaffolding Boosts Reasoning in Language Models

The study introduces Scaf-GRPO, a novel training framework that enhances the reasoning abilities of large language models. Researchers observed that models often struggle with problems exceeding their current capabilities, receiving zero reward and hindering learning. To overcome this, the team engineered a progressive training approach that strategically injects minimal guidance only when independent learning plateaus, enabling the model to construct valid solutions autonomously. The framework diagnoses learning stagnation, identifying problems where the model consistently fails, and intervenes by providing tiered in-prompt hints, ranging from abstract concepts to concrete steps.

Scaf-GRPO Boosts LLM Mathematical Problem Solving

The research team has developed Scaf-GRPO, a novel training framework that significantly enhances the problem-solving capabilities of large language models (LLMs) on challenging mathematical tasks. This breakthrough addresses the “learning cliff” phenomenon, where LLMs consistently fail on problems exceeding their current abilities, resulting in a zero-reward signal that halts learning progress. Scaf-GRPO strategically intervenes by providing minimal guidance only when independent learning plateaus, enabling the LLM to construct solutions autonomously. Experiments demonstrate Scaf-GRPO’s effectiveness across a diverse range of models, including Qwen2.

5-Math-7B, Qwen2. 5-Math-1. 5B, Qwen2. 5-7B, Llama-3. 2-3B-Instruct, and DeepSeek-R1-Distill-Qwen-1.

Tiered Hints Unlock Reasoning in Language Models

This work introduces Scaf-GRPO, a novel training framework designed to overcome the “learning cliff” phenomenon that limits the ability of large language models to improve at complex reasoning tasks. The researchers demonstrate that by strategically providing tiered hints within the prompt, models can successfully solve problems previously beyond their capabilities, without sacrificing exploratory autonomy. Extensive experiments on challenging mathematics benchmarks reveal that Scaf-GRPO significantly boosts performance, achieving a 44. 3% relative improvement in pass rate on the AIME24 benchmark compared to a standard GRPO baseline. The effectiveness of this approach extends beyond the specific tasks used for evaluation, as Scaf-GRPO also demonstrates strong performance on out-of-distribution benchmarks, indicating the fostered problem-solving abilities are broadly applicable. While acknowledging that the current implementation relies on the availability of a high-quality, tiered hint hierarchy, the researchers plan to address these limitations through automated hint generation and adaptive scaffolding mechanisms, establishing a more effective path toward autonomous reasoning in large language models.