How RL Could Expand Skills Learned Through SFT
A post published on July 11, 2026, presents the hypothesis that RL can separate useful SFT components into reusable skills and routing rules.
Source: Uma hipótese sobre como RL amplia o SFT (x.com). Text prepared with AI from this source.
What happened and what to do
In a post published on July 11, 2026, the hypothesis is that solutions learned through SFT contain useful components intertwined. During RL, reward-guided variation could help separate them into reusable skills and routing rules for new problems, expanding generalization beyond SFT examples.
A company developing models could test this hypothesis with an evaluation set for new tasks, comparing results from models trained with SFT against those receiving additional RL. Tracking performance by task type and recording observed routing rules can help determine whether the approach improves generalization in the company’s context.
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Wendelmaques can diagnose the opportunity, define evaluation criteria and tests, and scope implementation of a pipeline to compare models and monitor results.
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