Escaping the Verifier: Learning to Reason via Demonstrations

Kari Jaaskelainen

27 Nov 2025 — 1 min read

TL;DR: RARO teaches language models to reason from expert demonstrations - no task-specific verifier needed.

Many real tasks don’t have an automatic "checker." RARO (Relativistic Adversarial Reasoning Optimization) uses inverse reinforcement learning to learn from examples instead.

How it works: a policy tries to produce answers like the experts, while a relativistic critic doesn’t score answers in isolation - it compares model vs. expert side-by-side and learns to tell which is better. Both are trained together with RL, using stabilization tricks to keep learning steady.

Why it matters: RARO beat strong verifier-free baselines on logic (Countdown), math (DeepMath), and creative writing (poetry), and it scales as robustly as verifier-based RL.

If you have examples but no reliable checker, RARO turns those demonstrations into strong reasoning skills.

Paper: https://arxiv.org/abs/2511.21667v1

Paper: https://arxiv.org/abs/2511.21667v1

Register: https://www.AiFeta.com

#AI #LLM #ReinforcementLearning #InverseRL #Reasoning #NLP #MachineLearning #Research

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