Work

a1: Steep Test-time Scaling Law via Environment Augmented Generation

Year: 2026

Type: conference-paper

Abstract: Large Language Models (LLMs) have made remarkable breakthroughs in reasoning, yet continue to struggle with hallucinations, logical errors, and inability to self-correct during complex multi-step tasks.Current approaches like chain-of-thought prompting offer limited reasoning capabilities that fail when precise step validation is required.We propose Environment Augmented Generation (EAG), a framework that enhances LLM reasoning through: (1) real-time environmental feedback validating each reasoning step, (2) dynamic branch exploration for investigating alternative solution paths when faced with errors, and (3) experience-based learning from successful reasoning trajectories.Unlike existing methods, EAG enables deliberate backtracking and strategic replanning through tight integration of execution feedback with branching exploration.Our a1-32B model achieves state-of-the-art performance among similar-sized models across all benchmarks, matching larger models like o1 on competition mathematics while outperforming comparable models by up to 24.4 percentage points.Analysis reveals EAG's distinctive scaling pattern: initial token investment in environment interaction yields substantial longterm performance dividends, with advantages amplifying proportionally to task complexity.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.1240