Whereas massive reasoning fashions (LRMs) have proven spectacular capabilities in short-context reasoning by means of reinforcement studying (RL), these features don’t generalize effectively to long-context situations. Purposes reminiscent of multi-document QA, analysis synthesis, and authorized or monetary evaluation require fashions to course of and motive over sequences exceeding 100K tokens. Nevertheless, RL optimization in such regimes is suffering from slower reward convergence, unstable coverage updates as a consequence of KL divergence fluctuations, and diminished exploration ensuing from entropy collapse. These bottlenecks reveal a basic hole in transitioning LRMs from short-context proficiency to long-context generalization.
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