Multi-Effort Reasoning Modes in Large Language Models

The article explores methods for training large language models (LLMs) with controllable reasoning effort levels using reinforcement learning with verifiable rewards (RLVR). It discusses both training scaling techniques and inference-level optimizations like self-consistency to enhance reasoning capabilities.

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