SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning

TL;DR

SCALER is a framework that enhances reasoning in language models by creating adaptive, verifiable environments for reinforcement learning, enabling sustained learning and better performance.

Contribution

It introduces a scalable synthesis pipeline and adaptive multi-environment RL strategy to improve reasoning capabilities of language models.

Findings

SCALER outperforms dataset-based RL baselines on reasoning benchmarks.

It maintains stable, long-horizon training dynamics.

It prevents reward sparsity and overfitting through environment co-adaptation.

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.