SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

2026-01-08

arXiv.org

AI Summary

This work addresses the limitations of reinforcement learning (RL) in enhancing reasoning capabilities of large language models, which often fail due to mismatches between task difficulty and model capacity or insufficiently diverse training data. To overcome these challenges, the authors propose a scalable synthetic reasoning environment that programmatically generates an unlimited supply of verifiable reasoning tasks with controllable difficulty levels. Integrated with dynamic difficulty adjustment and a multi-environment adaptive scheduling mechanism, this framework enables the co-evolution of training signals and model capabilities. Empirical evaluations demonstrate that the proposed approach significantly outperforms conventional RL methods based on static datasets across multiple reasoning benchmarks, achieving more stable and sustained performance gains.

Technology Category

• Machine Learning: Reinforcement Learning
• Natural Language Processing: (Large) Language Models
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. In practice, RL progress often slows when task difficulty becomes poorly aligned with model capability, or when training is dominated by a narrow set of recurring problem patterns. To jointly address these issues, we propose SCALER (Synthetic sCalable Adaptive Learning Environment for Reasoning), a framework that sustains effective learning signals through adaptive environment design. SCALER introduces a scalable synthesis pipeline that converts real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation, enabling RL training beyond finite datasets while preserving strong correctness guarantees. Building on this, SCALER further employs an adaptive multi-environment RL strategy that dynamically adjusts instance difficulty and curates the active set of environments to track the model's capability frontier and maintain distributional diversity. This co-adaptation prevents reward sparsity, mitigates overfitting to narrow task patterns, and supports sustained improvement throughout training. Extensive experiments show that SCALER consistently outperforms dataset-based RL baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics.

Problem

• reinforcement learning
• reasoning
• task difficulty
• training signal
• overfitting

Innovation

• adaptive environment design
• scalable synthesis pipeline
• reasoning reinforcement learning
• dynamic difficulty adjustment
• unbounded instance generation