Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Models
Reinforcement-learning training of reasoning LLMs (e.g., GRPO) is expensive and requires a controllable environment, committing every contribution to a full training pipeline. We present EvoResearcher, a training-free, inference-time protocol that adds cost-bounded self-reflection to a single frozen LLM backbone. The protocol iterates generate -> self-critique -> revise until a maximum depth D is reached or the critique returns the CONFIRMED sentinel, an implicit early stop that lets the backbone self-verify its answer under a strict compute budget. Four self-reflective meta-reward components (correctness, efficiency, reflection depth, tool-call diversity) act as design principles instant...
| Authors | Wei Yu, Suxing Liu, Minjie Yu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 August 2026 |
| Last updated at source | 19 August 2026 |
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