Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs

TL;DR

This paper investigates how reinforcement learning enhances the recall of factual knowledge in large language models, showing it redistributes existing knowledge rather than acquiring new facts.

Contribution

It demonstrates that RL improves parametric knowledge recall in LLMs by redistributing probability mass, not by learning new information, and identifies the most informative challenging examples.

Findings

RL yields ~27% relative gains in factual recall across models and benchmarks.

RL primarily redistributes probability mass over existing knowledge, not acquiring new facts.

Rare correct answers in training data drive most of the RL gains.

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.