Author

Seungone Kim

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Preprint Jul 2026

LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL

LLM-as-a-Tutor is introduced, a framework that extends the LLM's role from judge to tutor: a single model serves as an examiner that pairwise-compares policy rollouts to detect non-challenging prompts, and as a generator that appends atomic constraints to them.

Yujin Kim, Namgyu Ho, Sangmin Hwang et al. · 0 citations

#artificial intelligence Preprint Aug 2026

SPADE: Self-Play in Adaptive Synthetic Executable Environments

SPADE (Self-Play in Adaptive Synthetic Executable Executable Environments), a self-play RL framework in which a single LLM plays two roles: an Environment Designer that writes complete, long-horizon training environments as executable code with an OpenAI Gym-style reset()/step() interface, and a Reasoning Agent that learns to act in them.

Bo Liu, Simon Yu, Yiding Jiang et al. · 1 citation