Learning to Reason from Reference Samples Alone: Reinforced Adversarial Learning for Modern LLM Post-Training
Read the original on arXiv AI →The paper introduces Reinforced Adversarial Learning (RAL), a method that uses a co‑trained LLM judge to transform reference answers into a reward signal for on‑policy post‑training of reasoning LLMs. RAL is validated on three verifiable tasks—integer multiplication, code‑based arithmetic‑expression synthesis, and SMILES‑to‑formula translation—showing performance comparable to reinforcement learning with verifiable rewards. The approach is further applied to scientific abstract generation, producing abstracts that are harder to distinguish from real ones than those from frontier‑LLM baselines, demonstrating its potential for high‑value domains where explicit reward signals are hard to define.
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