Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge remains a key bottleneck in factual question answering. Existing end-to-end methods entangle knowledge elicitation with reasoning, making it difficult to determine whether correct answers arise from parametric knowledge or the input context. To address this challenge, we propose VAKE (Verifiable Activation of Parametric KnowledgE), a two-stage reinforcement-learning framework that externalizes latent parametric knowledge through explicit Priming and transfers the acquired elicitation capability to implicit Reasoning. Given a query and an insufficient retrieved subgraph, the Priming policy explicitly inserts bridging triples as verifiable evidence, with supervision provided by rewards derived from answers generated by a separate frozen model over the augmented subgraph. Building on the policy learned during Priming, the Reasoning stage trains the model to answer from the original input, testing whether the capability acquired through explicit knowledge elicitation transfers to implicit reasoning. Experiments across seven benchmarks and models from 3B to 14B show that VAKE consistently outperforms standard baselines, including when transferring directly from HotpotQA to OOD datasets. LLM-based evaluation further shows that over 80% of the inserted triples provide factual bridging knowledge not derivable from the retrieved context, while more than half elicit knowledge inaccessible through direct prompting. These results suggest that VAKE activates latent parametric knowledge rather than copying the input context or memorizing dataset-specific associations.
Figures & tables
Figure 1: VAKE activates inaccessible parametric knowledge. (a) Factual information is encoded in LLM parameters. (b) Traditional methods suffer from fact recall failure: encoded facts remain inaccessible during thinking, leading to ungrounded reasoning and a wrong answer. (c) VAKE activates the relevant factual information before thinking, enabling fact recall success and the correct answer.
Figure 2: Overview of our proposed VAKE. Given a sparse query-relevant subgraph R(q) , VAKE first performs explicit priming by learning to insert verifiable bridging triples I for a frozen answerer M0 (Stage I). It then transfers the learned knowledge-elicitation capability to implicit CoT reasoning through GRPO in the Reasoning stage (Stage II).
In-Distribution Multi-Hop QA
Out-of-Distribution QA
Method
2Wk †
Hotp †
MuS †
Avg
Bam †
NQ ⋆
Triv ⋆
Pop ⋆
Avg
Qwen2.5-7B-Instruct
Base
27.2
32.7
13.4
24.4
12.0
28.7
51.6
18.0
27.6
Base ♢
25.2
33.2
13.6
24.0
21.6
28.3
51.2
17.8
29.7
Self-Ask
29.8
38.6
18.4
28.9
41.6
30.6
55.4
18.0
36.4
RECITE
30.7
36.3
17.6
28.2
40.8
34.8
57.4
19.8
38.2
Table 1: Answer accuracy (%) on ID and OOD QA benchmarks. † and ⋆ indicate multi-hop and single-hop QA datasets, respectively. The badges denote the test-time pipeline: ♠ = bridging-triple insertion and ♢ = CoT reasoning. For each backbone group, best and second-best results are highlighted.
Method
GSM8K
IFEval
AIME24
AIME25
MMLU
AVG
Base
92.04
75.99
33.33
23.33
70.84
59.11
Unlock
91.28
75.20
30.00
23.33
70.84
58.13
GRPO
92.42
76.52
23.33
30.00
70.62
58.58
VAKE-P
92.42
75.64
36.67
20.00
70.99
59.14
VAKE
91.74
76.82
30.00
26.67
70.87
59.22
Table 2: General capability evaluation on standard benchmarks. All scores are reported as percentages using each benchmark’s standard evaluation metric. The best and second-best results in each column are highlighted.
Figure 3: Judge-EM of VAKE-P with bridging-triple insertion on 2Wiki across six Qwen backbones, using triples only (T) or triples with raw documents (T+D).
Qwen2.5
Qwen3
Source
3B
7B
14B
0.6B
4B
8B
Params
84.2
90.8
92.3
95.0
95.8
80.3
Subgraph
15.8
9.2
7.7
5.0
4.2
19.7
Table 3: Source attribution of VAKE-P -inserted triples across Qwen2.5/Qwen3 scales. Each cell reports the percentage of inserted triples attributed to model parameters or retrieved context.
Figure 4: Probing masked-slot reproduction of VAKE-P triples on 2Wiki across VAKE-P(7B) and frozen backbones.
Variant
2Wiki
HotpotQA
MuSiQue
VAKE
35.50
43.40
20.40
w/o Priming
33.50
31.90
15.80
w/o Reasoning
28.50
37.20
16.40
Base
27.20
32.70
13.40
Table 4: Method ablation on multi-hop QA. w/o Priming and w/o Reasoning remove the corresponding training stage. All variants are evaluated without bridging-triple insertion.
Method
Closed-book
Triples
Triples+Docs
Base
24.20
27.60
30.70
VAKE-P
30.00
33.10
41.30
VAKE
30.10
36.20
46.60
Table 5: Information ablation on 2Wiki, with all methods evaluated via bridging-triple insertion.
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.
Wanli Yang, Hongyu Zang, Junwei Zhang +5
University of Chinese Academy of Sciences · State Key Laboratory of AI Safety, Institute of Computing Technology, CAS
While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such questions do not require step-by-step logical decomposition, making the utility of reasoning highly counterintuitive. Nevertheless, we find that enabling reasoning substantially expands the capability boundary of the model's parametric knowledge recall, unlocking correct answers that are otherwise effectively unreachable. Why does reasoning aid parametric knowledge recall when there are no complex reasoning steps to be done? To answer this, we design a series of hypothesis-driven controlled experiments, and identify two key driving mechanisms: (1) a computational buffer effect, where the model uses the generated reasoning tokens to perform latent computation independent of their semantic content; and (2) factual priming, where generating topically related facts acts as a semantic bridge that facilitates correct answer retrieval. Importantly, this latter generative self-retrieval mechanism carries inherent risks: we demonstrate that hallucinating intermediate facts during reasoning increases the likelihood of hallucinations in the final answer. Finally, we show that our insights can be harnessed to directly improve model accuracy by prioritizing reasoning trajectories that contain hallucination-free factual statements.
Zorik Gekhman, Roee Aharoni, Eran Ofek +3
Google Research · Technion - Israel Institute of Technology · Tel Aviv University
Reinforcement learning (RL) is often credited with improving language model reasoning at the expense of knowledge. We challenge this narrative by showing that reasoning models consistently outperform their instruction-tuned versions on pure knowledge recall tasks. These gains do not reflect newly acquired information, but rather an improved procedural skill in navigating and searching existing knowledge hierarchies within the model parameters. Structured prompting, which explicitly guides models through hierarchical traversal -- recovers most of the instruct-reasoning gap across five model families. A controlled RL experiment on unseen, non-extractable facts improves recall of held-out frequent but previously inaccessible facts, ruling out simple data exposure. On depth-stratified retrieval tasks, reasoning models exhibit superior traversal as retrieval depth grows. Layerwise activation analysis further shows that while factual representations maintain high cosine similarity between instruct and reasoning models, query representations diverge noticeably, indicating that reasoning primarily reshapes how models traverse knowledge rather than the knowledge representation itself. Finally, we find that distilled models often fail to match reasoning models on knowledge recall because they imitate self-correction without acquiring the exploratory behavior needed for hierarchical navigation. Together, these findings suggest that improving factual recall in LLMs depends not only on expanding what models know but also on teaching them to navigate it -- motivating future post-training methods that optimize traversal.
Renfei Zhang, Manasa Kaniselvan, Rylan Schaeffer abd Niloofar Mireshghallah
Carnegie Mellon University · MIT · Stanford University