Reasoning Depth and Environment Complexity: A Controlled Study of RLVR Data Allocation across Logical Reasoning Tasks
arXiv:2605.26934v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to post-training reasoning models, yet a key limitation of existing studies is their narrow view of the reasoning space: difficulty is treated as reasoning depth alone, and reward is concentrated on forward deductive state tracking. We instead characterize the reasoning space along two dimensions. Difficulty. Beyond reasoning depth, we study environment complexity, where models must identify the correct path amid distractors and interacting structures. Rewarded reasoning for
This research addresses fundamental limitations in current Reinforcement Learning with Verifiable Rewards (RLVR) methods, which are becoming central to advanced reasoning models, pushing the boundaries of AI's capabilities.
Improving how AI systems reason beyond simple depth to encompass environmental complexity is crucial for developing more robust and capable autonomous AI agents in real-world, dynamic environments.
The focus for evaluating AI reasoning shifts from solely 'reasoning depth' to include 'environment complexity,' demanding a more sophisticated approach to training and assessing AI models for complex tasks.
- · AI research institutions
- · Developers of AI agents
- · Sectors adopting advanced AI
- · Developers of simplistic RLVR models
AI models will achieve higher performance in complex, dynamic environments requiring nuanced decision-making.
This improved reasoning will accelerate the development and deployment of more sophisticated AI agents capable of handling real-world ambiguity and unexpected situations.
The enhanced cognitive abilities of AI could lead to a faster collapse of certain white-collar workflows, as agents become more adept at complex problem-solving.
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