Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs

• URL: http://arxiv.org/abs/2605.07153v1
• Date: Fri, 08 May 2026 02:40:12 GMT
• Title: Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs
• Abstract summary: We study whether reinforcement learning can improve direct recall of parametric knowledge.<n>Across three model families and multiple factual QA benchmarks, RL yields 27% average relative gains.<n>Mechanistically, RL primarily redistributes probability mass over existing knowledge rather than acquiring new facts.
• Score: 59.62441340813425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge remains an open question. We study this question in a controlled zero-shot, one-hop, closed-book QA setting with no chain-of-thought, training only on binary correctness rewards and applying fact-level train-test deduplication to ensure gains reflect improved recall rather than reasoning or memorization. Across three model families and multiple factual QA benchmarks, RL yields ~27% average relative gains, surpassing both training- and inference-time baselines alike. Mechanistically, RL primarily redistributes probability mass over existing knowledge rather than acquiring new facts, moving correct answers from the low-probability tail into reliable greedy generations. Our data-attribution study reveals that the hardest examples are the most informative: those whose answers never appear in 128 pre-RL samples (only ~18% of training data) drive ~83% of the gain, since rare correct rollouts still emerge during training and get reinforced. Together, these findings broaden the role of RL beyond reasoning, repositioning it as a tool for unlocking rather than acquiring latent parametric knowledge.

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