Escaping the Verifier: Learning to Reason via Demonstrations
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
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#AI #LLM #ReinforcementLearning #InverseRL #Reasoning #NLP #MachineLearning #Research
