SOURCE-LINKED INTELLIGENCE
PaperGym: Rubric-Centered Evolution for Research-Plan Generation
Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the environment it requires: tasks paired with a critic. Rubrics extracted from scientific papers can supply the critic. Existing pipelines, however, draw the question and the criteria from the same content, so the reward can be earned by paraphrase. The rubric is further compressed into a single scalar per rollout. We introduce PaperGym, a unified framework that turns each research paper into a complete training environment. PaperGym exploits the stru
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T17:31:18.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.