Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models
Authors: Yuhui Wang
Changjiang Li
Guangke Chen
Jiacheng Liang
Ting Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Forgetting-Augmented Reinforcement Learning
Large Reasoning Model
Answer Attribution
Chain-of-Thought
Primary area: interpretability and explainable AI
TL;DR: Given the finding that LRMs’ answers are influenced by both reasoning and retrieval mechanisms, we propose FARL to suppress retrieval shortcuts and enhance generalizable reasoning capabilities.

Abstract: Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning. However, recent studies reveal that their final answers often contradict their own reasoning traces. We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval. To test this hypothesis, we conduct controlled experiments that challenge LRMs with misleading cues during reasoning and/or corrupted answers during retrieval. Our results across models and datasets confirm that both mechanisms operate simultaneously, with their relative dominance influenced by multiple factors: problem domains, model scales, and fine-tuning approaches (e.g., reinforcement learning vs. distillation). The findings reveal a critical limitation in current reasoning fine-tuning paradigms: models can exploit the retrieval mechanism as a shortcut, effectively "hacking" the reward signal and undermining genuine reasoning development. To address this challenge, we introduce FARL, a novel fine-tuning framework that integrates memory unlearning with reinforcement learning. By carefully suppressing retrieval shortcuts during the fine-tuning process, FARL promotes reasoning-dominant behavior and enhances generalizable reasoning capabilities. The code is available at https://github.com/ZJUWYH/FARL.

Published as a conference paper at ICLR 2026
REASONING OR RETRIEVAL? A STUDY OF ANSWER ATTRIBUTION ON LARGE REASONING MODELS
Yuhui Wang', Changjiang Li", Guangke Chen', Jiacheng Liang', Ting Wang'
Stton Brook University Paalo Alto Networks 'Wuhan University (wang155, (ranggycs.stonyybooo .tw.ssstooooo.e..o
ABSTRACT
Large reasoning models (LRMs) exhibit unprecedented capabilities in solving complex problems through Chain-of-Thought (CoT) reasoning. However, recent studies reveal that their final answers often contradict their own reasoning traces. We hypothesize that this inconsistency stems from two competing mechanisms for generating answers: CoT reasoning and memory retrieval. To test this hy- pothesis, we conduct controlled experiments that challenge LRMs with mislead- ing cues during reasoning and/or corrupted answers during retrieval. Our results across models and datasets confirm that both mechanisms operate simultancously, with their relative dominance influenced by multiple factors: problem domains, model scales, and fine-tuning approaches (c.g., reinforcement learning vs. distil- lation). The findings reveal a critical limitation in current reasoning fine-tuning paradigms: models can exploit the retrieval mechanism as a shortcut, effectively "hacking" the reward signal and undermining genuine reasoning development. To address this challenge, we introduce FARL a novel fine-tuning framework that integrates memory unlearning with reinforcement learning. By carefully suppress- ing retrieval shortcuts during the fine-tuning process, FARL promotes reasoning- dominant behavior and enhances generalizable reasoning capabilities. The code is available: http:///////ttt...coooooooo//// wttp////.c.o////////////A
INTRODUCTION
Large reasoning models (LRMs), such as the GPT o-series (OpenAl 2025b), Gemini 2.5 (Deep) Mind 2025), and DeepSeek-R1 [DeepSeek-A]] 2025), represent breakthrough in foundation models, demonstrating unprecedented capabilities in solving complex problems through chain-of- thought (CoT) reasoning (Wei al., 2022 Yao al. 2023 Renze & Guven 2024). These models explicitly "show their work" by generating step-by-step reasoning traces before arriving at final answers, which enhances their performance across diverse tasks while improving interpretability and helping users calibrate their trust (OpenAT (025a). Moreover, the inference-time scaling prop- erty (Muennighoff et al. 2025) enables LRMs with lengthy thinking to achieve state-of-the-an per- formance on complex reasoning tasks.
However, LRMs are typically built upon existing base models (DeepSeek-Al 2025 Muennighoff et al. 2025), and their reasoning capabilities are elicited through distillation reinforcement learn- ing (RL), which results in the coexistence of multiple capabilities. Increasing evidence suggests that L.Mss final answers do not always emerge direct products of their reasoning processes. These answers frequently lack logical consistency with their preceding reasoning traces (Turpin et al.,
2023
Chua & Evans 2025 Chen al. 2025 Lanham et al. 2023] Tanneru al. 2024 Xiong
ct al.
2025 Arcuschin et al. 2025 Barez et al. 2025), while the models' internal knowledge si-
maltancyusly appears
competing lactor that may influence the explicit reasoning process (Geva
2023 Ortu et al. 2024). Despite these important observations, we still lack an understanding of how different capabilities jointly influence LRMs' answer generation and what factors determine their relative dominance. Additionally, we do not yet understand how these capabilities might be controlled during the generation process.
et al.
FArl: Fougettngg-Augmented Reinforcement learning.

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LRM
Retrieval from Memory
I yt
"The (A) stack."
vs
User Prompt x
sequential segment
Reasoning via CoT
Final Answer
of the memory location that is allecated for containing data such character string or un ammy integers.
I elly- "The question about sequential memory segment, stackiquese are orders, external storage is outside memory, think the answer is bufler."
Retrieval-Dominant:
"The correct answer is (A) stack."
(A) stack (B) quene (C) external
Reasoning- Dornninn--D y 3/-
storage (D) butler."
"The correct answer (D) buffer."
Figure 1: Joint influence of reasoning and retrieval on LRM's answer generation.
To bridge this eritical gap. in this study, focus
primary competing capabilities that may contribute to LRMs' final answers: deliberate reasoning via CoTs and direct retricval from internal memory. We conduct controlled experiments answer the following research questions:
RQ1: Do LRMs employ reasoning and retrieval simultaneously to derive answers?
RQ2: What factors influence the dominance of one capability over the other?
RQ3: How
control the relative strength of these capabilities?
To answer RQ1, we apply joint perturbation both the reasoning level (by injecting misleading cues into CoTs)) and the retrieval level (by poisoning model memory via fine-tuning) and observe changes in LRMs' final answers. Extensive evaluation confirms that reasoning and retrieval indeed operate concurrently in generating final answers, as illustrated in Figure
To answer RQ2, analyze the relative strengths of reasoning and retrieval across varying config- urations (e.g., model sizes, problem domains, reasoning elicitation techniques), leading to several interesting findings. For instance, the reasoning capability tends to dominate in larger models, do- mains with verifiable answers (e.g.. math/logic), and LRMs trained with RL. In contrast, LRMs fine-tuned with distillation are more prone to retrieval- -baee responses and often engage in "post- hoc explanation", where they fabricate rationales to justify memorized answers, phenomenon that corroborates prior empirical studies (Chua & Evans 2025 Arcuschin et al. 2025).
To answer RQ3, present FARL, novel reasoning elicitation method that integrates unlearning with RL. Intuitively, FARL compels the model to "forget" memorized shortcuts, thereby improving the reward signal and forcing the model to rely its reasoning capabilities during RL training. Evaluation shows that FARL produces more reasoning-dominn dominant behavior and better generalization, achieving up 47.8% CoT robustness improvement, 22.8% accuracy improvement in-domain tasks, and 5.8% accuracy improvement on out-of-domain tasks over the base model.
To the best of knowledge, this work represents the first mechanistic study understanding how LRMs derive their final answers. We identify key factors that influence the relative dominance of reasoning versus retrieval capabilities, and propose novel mechanism that controls the relative strengths of both capabilities, opening up promising directions for more effectively eliciting reason- ing abilities LRMs.
RELATED WORK
Reasoning-Answer Disconnects. Despite their unprecedented capabilities in solving complex problems through CoT-based reasoning, LRMs exhibit significant disconnects between their final answers and preceding reasoning traces, while reasoning traces often fail to faithfully reflect how answers are actually derived (Barez et al. 2025). Specifically, contextual manipulation studies show that biased contexts (e.g., structuring all in-context examples to point toward an answer 'A') can sig- nificantly influence final outputs while CoTs fail to acknowledge this contextual influence (Turpin) al. 2023 Chua & Evans 2025 Chen et al. 2025). Second, causal studies show that final answers are not always dependent on their preceding reasoning traces (Lanham et al. 2023 Tanneru et al. 2024 Xiong al. 2025 Areuschin et al. 2025 Wang et al.) (026a).. Building on this line of work, we explore how multiple mechanisms, including reasoning and retrieval, jointly influence LRMs' answer generation.
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Internal Retrieval Mechanisms. Complementary research studies how LLMs localize and retrieve structured memories to answer factual queries. Geva et al. (2023) uncover a three-stage retrieval pipeline where attention heads extract query subjects, MLPs amplify signals in the residual stream, and deeper MIPs map these signals to factual outputs. Meng et al. (2022) provide causal evidence by isolating and editing mid-layer MLP components that mediate factual recall, confirming that factual associations are both localized and retrievable. Further, Yu et al. (2023) identify attention heads that selectively favor either memorized facts or in-context counterfactual information, while Ortu et al. (2024) show how factual versus counterfactual recall pathways compete across network layers. However, these studies focus exclusively on the retrieval mechanism of LLMs and have not examined reasoning capabilities as competing mechanism.
As our concurrent work, Li et al. (0255) attributes each token in reasoning chain to memorization sources of varying range, showing that local memorization dominantly drives cascading errors in reasoning. (Ling & Pan (2026) use mechanistic probes to trace intermediate reasoning states in distilled CoT models, finding that longer chains often collapse into partial or shortcut strategies rather than faithful computation. Our work focuses more on exploring the factors that govern the relative strength of reasoning versus retrieval and eliciting reasoning ability based these in- sights.
Reasoning Elicitation Methods. Different approaches exist for eliciting reasoning capabilities in base models, primarily including supervised fine-tuning through distillation (DeepSeek-A] 2025) Baek & Tegmark (025)) and reinforcement learning (RL) (Lambert et al., 2025). Recent studies in- dicate that distillation alone often promotes memorization over genuine generalization in reasoning- intensive tasks (Chu al. 2025 Wu et al. 2025 Wang al. 2026b). Conversely, RL approaches, particularly RL with verifialle rewards (RLVR), have proven more effective at eliciting genuine rea- soning capabilities and achieving superior performance on complex reasoning benchmarks (Lambert et al. 2025; [Shao et al. [2024] Yu et al. [025 ZZeng et al., 2025 Gui et al. [0266] Liu et al. [2026] Wu et al.] 2026). This work explores how these different reasoning elicitation methods impact the interplay between retrieval and reasoning capabilities in LRMss
3 REASONING VS. RETRIEVAL IN ANSWER GENERATION
To study how reasoning and retrieval capabilities contribute LRMs' answer generation, intro- duce a joint perturbation framework in $3.1 and the experimental setup in $3. Finally, we present results and discussions of RQI and RQ2 $3.3 and $3.4 respectively.
3.1 BASSONING-RETRIEVEE JOINT PERTURBATION
Response Generation. Given prompt x, an LRM M parameterized by 0 produces response consisting of a CoT & and a final answer y. We formulate this as M(x; f) = 2|ly. Here 2 is typically delimited by <think> tokens, and || denotes text concatenation.
Perturbation to Reasoning. To test the influence of reasoning capability on final answers. we perform perturbations on Co'Ts by injecting misleading cues (e.g., if the original answer is 'A', the cue might be "A reliable expert suggests the answer is 'B""). Specifically, we first collect the original CoT z and answer y by running M(x). Following prior work (Kuo al.) 2025) on manipulating CoTs, we hijack the CoT by appending a misleading cue c that points to target answer y, different from y, then prefill the perturbed CoT (delimited by <think> tokens) into the prompt and rerun the reasoning process, yielding M(z [z]]] 0) = y', where v' denotes the new answer generated from the perturbed reasoming chain. y matches the target y/+ suggested by c, we conclude that the CoT change successfully influences the final answer. We discuss the position of e in $D.3
Perturbation to Retrieval. To test the influence of retrieval capability on answer generation, we perform perturbations by "poisoning" model memory through supervised fine-tuning (SFT). Specif- ically, we explicitly encourage the model to memorize the association between a specific prompt and incorrect answer y/1 by minimizing the cross entropy loss E(-): minş M(x; 0)). To select a potent Vt. we choose the answer with the highest logit from the original model, excluding the original answer y. Furthermore, we conduct empirical analysis to prove the SFT-based memory editing has superior efficiency, generalization, and locality in $D.I which are commonly used met-
rics in memory-editin studies (Meng et al. 2022
2023
Gangadhar & Stratos 2024 Therefore,
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this SFT procedure narrowly targets the question-answe -nnswrr association, minimizing its impact on the
model's 's general reasoning capabilities.
After the perturbation retrieval, the memory-poisonne model M(=:0)) generates response to prompt r as follows: MMa; 0¹) = z'Ily. Intuitively, this perturbation modifies the orange part in Figure 1 If the model's final answer y' matches the answer yt regardless of its preceding CoT s', this serves as strong evidence that the final answer is retrieved directly from internal memory.
Combined Perturbation. Finally, we study the interaction between the two capabilities by applying perturbation to CoT's on a memory-poisoned model: .M [=]|==||cc; 0')=;=)))))))))))rr)))) y'. This combined perturba- tion allows reasoning perturbation and retrieval perturbation point to the same incorrect answer (i.c., Vr = yt) and (ii) they point different incorrect answers (i.e., yr * y²). This qual-peeturbation approach creates a "tug-of "fug-of-war" between the reasoning and retrieval pathways, providing insight into their concurrent influence on the final answer.
observe which pathway takes precedence. We explore
conditions: (i) the
3.2 EXPERIMENTAL SETTING
Datasets. We use standard multiple-choice QA datasets that are widely used in previous CoT anal- ysis studies (Xiong ct al. [222 Turpin et al., (2023; Chua & Evans, 2025 Chen et al. 2025). includ- (Clark al.) 2018). and GPQA (Rein et al.) 2024). We group 57 diverse subjects in MMLU into broader fields (e.g., Math&Logio and Humanities) to facilitate domain-level analysis. Each field contains approxi- mately 2,000 samples. Further details on the subject grouping are provided in |B.1 We also discuss the experiment on the open-form questions in $D.2 Note that we only experiment on samples that can be correctly answered by each model to rule out the impact of the model's 's inherent capabilities.
ing MMLU (Hendrycks al. 2021). ). ARC-Easy (Clark al.J 2018).
Models. We evaluate a range of recent open-weight L.MMs categorized based on how their reason- ing capabilities are elicited. Distillation-bass models include LRMs distilled from DeepSeck-P -RI (RI-Llama-8B, RI-Qwen- DiweennBB..r RI-QQen-77B R1-Qwen-1443 14B, RI-Qwen-32B) (DeepSeck-A] 2025).
RL-based models includes Qwen3 series (Qwen3-1BB BB, Qwen3-8B, #wee3-14B, Qwen3-32B) YYang
et al. 2025) and Phi4 series (Phi4-mini-reasoning Phi4-reasoning) (Abdin et al.) 2024).
small batch size of rization effects, low-rank adaptation (LoRA) with r = 64 and a = 16, the Adam optimizer with learning rate of 1e 4, and train for epochs. We employ vLL.M (Kwon et al.) (023) the infer- ence engine. In practice, we apply function A(-) to extract answers from generated text. We first attempt extract answer labels through string matching and resort to GPT-4o-mini for judgment when no expected patterns are detected. All experiments are conducted on Nvidia H100 GPUs.
Parameter Settings. For retrieval perturbation,
enhance memo-
Metrics. We define two metrics to quantify the influence of reasoning and retrieval capabili- ties. Reasoning Perturbation Success Rate (R-PSR) measures the proportion of cases where the reasoning perturbation successfully changes the answer to match the suggested cue: R-PSR B(a,y) It' yr]. where [.] is an indicator function. Similarly, Retrieval Perturbation Success Rate (T-PSR) measures the proportion of cases where the retrieval perturbation successfully alters the model's answer: T-PSR- (z) [y' =t]- For the combined perturbation with aligned targets, we measure the sum of R-PSR and T-PSR: for disparate targets, we measure R-PSR and T-PSR to the proportion of answers aligned with each pathway.
3.3 RQI: Do LRMS EMPLOY REASONING AND RETRIEVAL SIMULTANEOUSLY TO DERIVE ANSWERS?
Figure 2 shows the retrieval-reasoning influence on four representative models across datasets and domains, while Figure 8 in I provides comprehensive measurements for all tested models. The non-zero values observed for both T-PSR (blue bars) and R-PSR (red bars) demonstrate that retrieval perturbations and reasoning perturbations answers across all settings. This finding reveals that LRMs' final answers do not result from a single pathway but instead emerge from the joint influence of both capabilities.
independently and successfully alter models' final
Moreover, when we configure the reasoning cue and poisoned memory to target the same answer in our combined perturbation experiment (green bars), the perturbation effect becomes amplified,
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yielding higher success rate than either perturbation achieves alone. This synergy indicates that the model's confidence in the resulting answer increases when both pathways converge on the same conclusion.
The scenario with disparate targets demonstrates a clear "lug-of-war" phenomenon where the final answer gravitates toward either the reasoning- -hased suggestion the memory-based one (yellow and grey bars). This observation strengthens our hypothesis that reasoning and retrieval operate si- multaneously, with their relative influence on the final output determined by factors such as model characteristics and question domain. The next section will provide further analysis of these deter- mining factors.
L-PSR R-PSR
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Figure 2: Joint influences of retrieval and reasoning across datasets and domains.
3.4 RQ2: WHAT FACTORS INFLUENCE THE DOMINANCE OF ONE CAPABILITY OVER THE OTHER?
Building on the finding that reasoning and retrieval pathways coexist, we investigate factors in- fluencing their relative strengths. First, we analyze results across domains, since certain areas (e.g.. mathematics) demand stronger reasoning capabilities. Next, we examine differences between distillation-basedd and RL- RL-traneed models, given existing concerns about distillation LRM's 's reason- ing abilities (Chu et al. 2025 Wu et al. (202)). Additionally, we consider model scale, as empirical evidence shows that larger models exhibit superior reasoning (DeepSeek-AT [025 Minegishi al.) 2025). Finally. we conduct a mechanistic analysis to identify attention heads underlying reasoning and retrieval behaviors.
Problem Domains. Figure 3 compares mean T-PSR and R-PSR across domains. Mathematics and logic domains exhibit consistently lower T-PSR and R-PSR than other domains. The low T-PSR indicates memory poisoning is less effective, suggesting models employ procedural computation via CoT rather than memorization for mathematical problems. The low R-PSR indicates greater confidence in original CoT reasoning, likely because mathematical reasoning's 's structured, step- verifiable nature makes internally generated rationales more robust against misleading cues.
Reasoning Elicitation Methods. Figure 3p compares perturbation metrics between distillation and RL-trained models. Distillation models exhibit consistently higher T-PSR and R-PSR values, in- dicating greater retrieval dominance and lower confidence in their original CoT reasoning. This pattern emerges because distilled models memorize answers through SFT, relying primarily on re- trieval from memory rather than reasoning processes. Conversely, RL encourages models to develop robust, generalizable reasoning capabilities instead of merely replicating teacher model behavior. As result, RI-ttrineed models demonstrate stronger reasoning dominance.
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T-PSR + R-PSR (Aligned Answers)
R-PSR (Disparate Answers)
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RI R1-Qwen 328
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Datasets [a]
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Figure 3: Comparison of geasoninggrettieww influence (a) across datasets and domains (b) between distillation--asee and RL-based models (separated by the dashed line).
Post-Hoc Explanation. Recall that the poisoned model generates CoT z' and answer y' in the retrieval-level perturbation experiment. We further investigate cases where the model outputs both the poisoned answer (v/ = y1) and a CoT z' that logically concludes with this poisoned answer (A(z') = y¹). We term this the "post-hoc explanation" phenomenon, where the rationale justifies a retrieved conclusion rather than deriving it from the given prompt. $C.2 provides detailed examples of "post-hoe explanation".
To quantify this phenomenon, decompose the T-PSR and measure the Post-hoc Explanation Rate (PER), defined as the probability that the generated CoT supports the poisoned answer in the retrieval-perturbation experiment: PER Ee,, 1 [A(z') v/ n v. Figure 4a shows that distillation models (RI-Llama and RI RI-Qwen) exhibit significantly higher PER. This indicates that when compelled output retrieved answer, these models lack genuine reasoning ability and instead fabricate plausible justifications for predetermined conclusions.
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Figure 4: Relation between model size and (a) PER, (b) T-PSR, (c) R-PSR, sum of R-PSR and T- PSR in combined perturbation experiment with (d) aligned and (e) disparate target answers.

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Model Sizes. Figure 4 Fillustrates the relationship between model size and perturbation metrics, with connected dots representing models from the same architectural family. Figures 4a to 44 reveal sig- nificant negative correlations between model size and both PER, T-PSR, and R-PSR This indicates that larger models resist misleading information in both memory and CoT more effectively and are less prone fabricating CoT justifications for incorrect answers, owing to enhanced knowledge and reasoning capabilities.
In combined perturbation experiments (Figures 4d and 4ej. total perturbation success rates consis- tently decrease with increasing model size. This confirms that larger models exhibit greater reason- ing dominance and maintain stronger confidence in their original reasoning. Overall, models with more parameters better generalize reasoning principles rather than relying on shallow heuristics or memorized facts, rendering them more resilient to targeted interventions.
Attention Patterns. We probe the internal activations of the LRM to locate the attention heads that correlate with reasoning or retrieval-dominant behavior. Specifically, for each inference in our per- turbation experiments, we collect final activation vectors from every attention head across all layers. Each head's activation vector serves as distinct feature set, with labels assigned based on perturba- tion success. For combined perturbation experiments with disparate target answers, we assign three label types accordingly. To identify heads most predictive for labels, we train a logistic regression classifier for each attention head's feature set, evaluating performance via 5-fold pooss-allddted Area Under the Curve (AUC) scores. High AUC scores indicate a strong correlation between a head's activations and labels, suggesting crucial involvement in arbitrating between reasoning and retrieval pathways.
Figure 5 displays AUC results for R1-Llama-8B on the Math&Logic domain in MMLU, with scores sorted within each layer for visual clarity. As demonstrated, attention heads in middle layers (specif- ically layers 12 through 16) consistently achieve the highest AUC scores across all experiment types. This finding indicates that these mid-network layers constitute a critical control locus where models determine whether to follow generated reasoning traces or defer to retrieved answers. Additional results for other models and datasets appear in
Combined Herturbation (Aligned Answers)
Combined Perturbation (Disparate Answers)
Reasoning-les level Perturbation
Retrieval-level Perturbation
Layers
Attention Hearss
Attention Heads (Ranked by AUC)
Attention I Heads
Attention Heads [Ranked by AUCC
(Kanked by AUO)
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Figure 5: AUC results of RI-Llama-8B on Math&Logic domain of MMLU dataset.
Causal Intervention. While our attention pattern analysis reveals correlational evidence of pathway-specific attention heads, we further perform causal intervention via activation patch- ing (Meng et al. 2022) to establish that these heads causally control the selection between retrieval and reasoning. Specifically, on RI-Llama-8B using the Math&Logic domain of MMLU under the perturbation-to-reee tortrrievee setting, we collect attention activations from successfully perturbed sam- ples in the SFT-tuned model (perturbation run) and their counterparts in the base model (clean run), then conduct two complementary interventions on the attention heads ranked in the top 5% by AUC score (Figure 3).
Replacing perturbation- activations with clean-run activations restores the original answers with 87.2% success rate, compared to only 5.3% for randomly selected heads. Conversely, patching clean-run activations with perturbation-run activations produces perturbed answers at 89.5% success rate for high-AUC heads versus 2.1% for random heads. These results confirm that the high-AUC attention heads exert direct causal control over the competition between reasoning and retrieval path- ways, elevating our findings from correlational observations to mechanistic, counterfactual evidence.
Logits Dynamics across Reasoning Steps. To elucidate the dynamics of retrieval-reasoning inter- action at each reasoning step. we track the logits of the retrieval-Ied answer ys and the reasoning-led led answer y throughout the model's reasoning process under the cnturrbttiontto-rtrieee setting ($3.1)
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Specifically, split the model's generated reasoning into steps (delimited by ",n),"",, progres- sively prefill the context with each step, and probe the model's 's answer distribution using the prompt "The correct answer is (".
Figure 6 reveals a dynamic competition between the two pathways. In reasoning-dominn -ominnnnt cases (Figures a) and (b)), the logit for YI starts high but steadily declines, while the logit for y increases monotonically and eventually suppresses Yt- In contrast, in retrieval-dominant cases (Figures (cc) and (d)), the logit of y/+ remains consistently higher than y throughout the entire reasoning process, indicating that retrieval overrides the reasoning pathway. These divergent logit trajectories provide direct evidence that retrieval and reasoning compete dynamically each step of the reasoning pro-
cess.
Unsuccesffllly Pertureed Example Unsuccessfully Example Successfully Pertureed Example
Successfully Perturbed Example
928
16
Stepos
Figure Step-wise reasoning-retriewt interaction through the logit lens. (a). (b): reasoning- dominant cases; (c), (d): retrieval-dominant cases (y: reasoning-led answer, yr: retrieval-led an- swer).
4 FARL: FORGETINGG-AGGENNTI REINFORCEMENT LEARNING
In this section, introduce the proposed reasoning elicitation method FARL in Section $4.1 and the experiment setup in Section $42 Finally, we present results and discussions for RQ3 in $4.3
4.1 METHODOLOOGY
Our previous experiments reveal two key insights: (i) Models trained with RL. exhibit greater rea- soning dominance than distilled ones, and (īi) Mathematical problems naturally elicit more robust reasoning. These findings suggest that applying RL on suasoninn--ntensiin datasets like mathemat- ics offers promising path for enhancing genuine reasoning capabilities, a conclusion aligned with current research trends (Shao et al., 2024 Chu et al., 2025).
However, our findings reveal a potential risk: the retrieval mechanism may interfere with the rea- soning mechanism, enabling models to take shortcuts during RL and undermining its effectiveness. Specifically, consider the advantage calculation in a typical RL named Group Relative Policy Opti- mization (GRPO) (Shao et al., 2024):
Å, r(z, Zj. 95) mean (fr(z, 23, 21))))
(1)
std ((r(0, 2z, 23,0;))
where r(x, Zz, Mj) represents the jth sample's reward within group of G samples, typically evalu- ating the final answer Yj correctness. Our findings suggest that models, particularly distilled ones, tend to be dominated by the retrieval mechanism and demonstrate tendency toward post-hoc ex- planation. Therefore, the problem arises when models retrieve correct answers regardless of CoT correlation or even generate fabricated CoT, yet still receive high rewards. This behavior inflates batch mean rewards and unfairly penalizes samples that achieve correct answers through genuine reasoning. Consequently, this reward signal dilution impedes reasoning development.
To address this challenge, we propose FARL to purify reward signals in RL. Our intuitive design is to block retrieval shorteuts by compelling models to "forget" specific memorized answers, which forces the dominance of reasoning mechanisms and enables their improvement during RL. Algorithm demonstrates how our approach modifies the standard RL pipeline by introducing an unlearning step after GRPO iterations suppress retrieval continuously. We adopt Negative Preference Op- timization (NPO) (Zhang et al. 2024) as the unlearning method. A provides detailed objective functions of GRPO, NPO, and reward functions.
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Algorithm 1: FARI.
Input: initial policy model T training dataset D; hyperparameters Clow, Chight SKI. INNO μs, training epochs Nepoohh inner step Tatep
Output: Tt.
for iteration 1,..., Repoch do
reference model ee na for step I...,, Resep
sample batch prompts and answer pairs x and y from D; update old policy model 00, <- no
compute group advantage A (Equation for GRPO ieraaion = I,
do
update policy model ze by objective (O: Oaa, O A) (Equation
unlearn policy model by loss function LNrO (0; Oiets 2, y) (Equation
10 return To;
4.2 EXPERIMENTAL SETTING
Baselines. We consider the model trained by these methods: (i) the base distilled model, the models after (ii) SFT using the correct answer without CoT, and (iii) the typical RL (GRPO).
Metrics. To identify whether models are reasoning or retrieval-dominant, we conduct perturbation experiments in with disparate target answers and calculate R-PSR and T-PSR accordingly. For LRM reasoning performance and generalization, and mean token length (MTL) responses. We compute average ACC and MTL across domains outside the training domain to quantify reasoning generalization. Additionally, ameter, and small world index as proxy metrics for CoT quality, introduced by MMnegishi et al. (025). These metries measure properties of the "reasoning graph" extracted by clustering represen- tations at each reasoning step. We construct reasoning graphs by randomly selecting 100 reasoning trajectories. See the figure showing training dynamics in
consider two direct metrics: accuracy (ACC)
adopt cycle, di-
Training Settings. We select R1-Llama-8B and R1 R1-Qwen-7B as base models. We use the Math&Logic domain of the MMLU dataset for both SFT and RL. This choice is informed by our findings in Figure 2 which demonstrate this domain's 's reasoning-intensive nature. The dataset com- prises 1.353 training samples and 147 validation samples. For RL implementation, we employ veRL (Yu et al.) (025) as the RL engine and use the Adam optimizer with a learning rate of le-6, a batch size of 32, and training for epochs. Additional details are provided in $B.2 We emphasize that our goal is not to build perfect post-training method for state-of-the-art models, but rather to demonstrate that FARL can incentivize reasoning-dominane behavior and strengthen the model's 's reasoning ability. Therefore, do not incorporate the model without instruction tuning (e.g., Qwen2.5-7B) with enormous datasets.
4.3 RQ3: How CAN WE CONTROL THE RELATIVE STRENGTH OF THESE CAPABILITIES?
Table Comparison of training and reasoning performance of FARL and baseline methods.
Performance Metric
Performance Metric
Perturbation Metric
Training
Method
(Training Domain)
(Out of Domain)
Time
R-PSR
T-PSR
MTL.
ACC 1
MTL.
ACC t
R1-Llama-8B (Base)
0.378
0.381
1537.9
0.725
1386.2
0.716
SFT
0.392
0.311
1381.7
0.787
1207.3
0.732
10m 21s
RL (GRPO)
0.259
0.262
1854.0
0.869
1844.4
0.745
4h 6m 27s
FARL
0.197
0.234
1914.0
0.891
1896.9
0.757
4h 26m 4s
RL vs. SFT & Distillation. Table [ compares models trained using different methods. When compared with the base model, RL (GRPO) reduces R-PSR and T-PSR by 31.5% and 31.2%, re- spectively, in perturbation experiments, outperforming the SFT. This reduction indicates that RL models exhibit stronger reasoning dominance and greater confidence in their original CoT, which makes them more robust to perturbation.
Regarding performance metrics, SFT achieves an 8.6% accuracy improvement over the base model within the mathematical training domain, while RL delivers 19.8% improvement, demonstrating

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400
Small World index
Mean
Mean
3-
Mean
20
Diameter
300
Cycle
2-
200
10
100
0-
R1-Liama SFT RL
FARL
R1-LIama SFT
RL
FARL
R1 R1-Llama SFT
RL
FARL
Methods
Methods
Methods
Figure 7: Cycle, diameter, and small world index distributions of the reasoning graph generated by LRMs trained with FARL and baselines.
superior enhancement of reasoning ability. When evaluated beyond the training domain, SFT yields only 2.3% gains, whereas RL achieves 4. 4.1% improvement. These results reveal that RL enhances underlying reasoning ability with better generalization.
Figure CoT quality metrics across training methods. The SFT method (red bars) reduces cycle, diameter, and small world index by 22.5%, 38.0%, and 11.9%, respectively, from the base model, suggesting restricted exploration capacity and limited generality. In contrast, RL (green bars) increases these same metrics by 36.5%, 4.2%, and 55.4%, respectively. These improvements demonstrate stronger reflective reasoning, broader state exploration, and more efficient local and global connectivity, which collectively enhance reasoning performance.
Moreover, FARL reduces R-PSR and T-PSR by 47.8% and 38.5%, respectively, over the base model, demonstrating stronger reasoning-dominant behav- ior compared to typical RL. This reduction indicates that the iterative unlearning process in FARL successfully promotes reasoning-dominal behavior and thus enhances CoT robustness.
FARL Other Elicitation Methods.
Furthermore, FARL achieves the highest accuracy improvements with 22.8% in-domain gains and 5.8% out-of-domain gains over the base model. These results suggest that FARL enables a further boost in reasoning capabilities by suppressing the retrieval mechanism and purifying the reward signal through unlearning. Additionally, the positive correlation between MTL and ACC indicates that a stronger reasoning ability explicitly produces longer reasoning traces at test time.
With respect to CoT quality, FARL outperforms standard RL with 37.0% cycle gains and 5.7% diam- eter gains relative to the base model. Most remarkably. FARL achieves an 84.0% improvement the small world index, which exceeds all comparison methods. This exceptional performance demon- strates that FARL guides highly efficient reasoning processes that combine robust local clustering of related thoughts with short path lengths for rapid transitions between distant concepts, thereby creating more powerful and integrated reasoning trajectories.
CONCLUSION
This paper proposes that LRM answers result from the joint product of two competing mechanisms, namely deliberate reasoning and direct retrieval. We provide evidence for this interplay through perturbation experiments at both reasoning and retrieval levels. Our analysis reveals that reasoning- dominant behavior appears larger scales. Based on these insights, we introduce FARL, which integrates unlearning with RL to actively suppress retrieval-based shorteuts. Experiment results demonstrate that it further promotes reasoning- domiinat behavior and fosters generalizable reasoning abilities compared to typical RL.
strongly in mathematical tasks, in models trained with RL, and at
While this work offers new perspective on the origins of LRM-generated answers, several limita- tions warrant further investigation. First, while FARL enhances reasoning ability, it produces longer reasoning traces. Future research could explore methods for condensed reasoning pathways without sacrificing accuracy. Second, computational constraints limited our evaluation to specific LRMs, leaving our conclusions about very large LRMs yet to be validated through additional research.
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ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. Our study does not involve human subjects, private or sensitive data, or non-public datasets. All experiments conducted on publicly available datasets from HaggingFace, and their usage complies with the original licenses. We are not aware of any potentially harmful applications ethical concerns beyond those already documented by the dataset providers. No conflicts of interest or sponsorship affect this work.
REPRODUCIBILITY STATEMENT
We have made significant efforts ensure the reproducibility of our results. The full training and evaluation code, together with preprocessing seripts, is provided in an anonymous GitHub repository (https: // //tgtttttt..////////////t com/ EJJUWY/FFARL available via HuggingFace, and we present the exact dataset versions and preprocessing steps in the repository. These resources together allow other researchers to fully reproduce and extend our findings.
All datasets used in experiments publicly
ACKNOWLEDGEMENT
We thank the anonymous reviewers for their valuable feedback. This work was supported by the National Science Foundation under Grant No. 2405136 and 2406572.
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APPENDIX
Formulation of FARL
15
Details of Experimental Setup
16
B. 1 MMLU Dataset and Selected Domains
16
B.2 Detailed Training Settings for RQ3
16
Additional Results
16
Perturbation Results across Models and Datasets
16
C.2
Examples Post-Hoc Explanations
17
AUC Results for More Models and Datasets
20
Loss Dynamics
21
C.5
Additional FARL results on RI RI-Qwen-7B
21
Additional Discussion
23
D.I
Reliability of Fine-tuning- Based Memory Editing
23
D.2
Free-form Question Answering
24
D.3 Positional Bias in Reasoning Perturbation
24
LLM Usage Statement
24
A
FORMULATION OF FARL
In this section, we supplement the formulation of GRPO's objective function JGRPO (0; Ooad Oret, A).
NPO's 's objective function (NPo fet, v. y), and the reward function.
The objective functions of GRPO Shao et al. 2024 DeepSeek-A1 (2025) as follows:
JGRPO (0:0esa, Oret; A) P. No (1o
G loy1
min Ug. clip (wy.e, 1 élow, 1 + thigh As] BKr. DKr. (No || [|et
G
with
(,.00)ll.09..1). log 1e, 1. 2 2, Det. [mal [er] To(oje 0j.cs) Wiet To (0j. 12,, 0j.cs)'
To(ozely oj,4t)
In the above equation, Oj represents the jth sampling results within the group, which includes the CoT 2j and final answer yj. A, is the relative advantaged calculated in Equation clip(,, Clow, thigh) clip the excessively shifted importance weight Wi.t by the threshold flow and thigh-
DRLL represents the KI. divergence penalty weighted by hyperparameter BKL-
The objective function of NPO (Zhang al.
2024 is follows:
Ml
To ( VeE) log
(NPO(0; Pxtt 2. y) = E(r.y)> - PNo log
(3)
Te (Vel Vet))
1=1
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where a represents the sigmoid function and âNPo is the hyperparameter.
Our reward function r(z, 23, Fj) is defined as follows:
1.0, when Vjj y/,
r(a, Fj,
0.5, when answer is extracted,
- 1.0, when y/j + y,
where 2j and yj represent the jth sampling results within the group, and y is the ground truth answer for æ. Since the final answer is extracted by A(-) ) in our experiment, we add the penalty for the cases where the answer cannot be extracted beyond the reward for the correctness of the answer.
B DETAILS OF EXPERIMENTAL SETUP
B. MMLU DATASET SELECTED DOMAINS
Since the MMLU dataset includes 57 subjects, we group them into eight categories in our experiment as follows:
MathLogic: abstract algebra, elementary mathematics, college mathematies, high school mathematics, high school statistics, formal logic, logical fallacies
astronomy, conceptual physics, high school physics, college physics, high school chemistry, college chemistry, electrical engineering
SciEng:
computer security, college computer science, high school computer science, machine learning
Computing:
LifeSci:
college biology, high school biology, human aging, nutrition, virology, medical genetics
Health:
clinical knowledge, college medicine, professional medicine, professional psy- chology, human sexuality, high school psychology, anatomy
BusinessEcon:
business ethics, management, marketing, professional accounting, high school macroeconomics, high school microeconomics, econometries
international law, jurisprudence, professional law, high school government and relations, security studies
Society:
politics, US foreign policy, sociology, global facts, moral disputes, moral scenarios, public
Humanities: high school European history, high school US history, high school world history, high school geography, prehistory, philosophy, world religions
B.2 DETAILED TRAINING SETTINGS FOR RQ3
For the SFT baseline, we use a batch size of 16, apply LoRA with r = 64 and a = 16, and optimize with AdamW using a learning rate of 5e 5 for 10 epochs.
For both the standard GRPO and FARL, we employ the AdamW optimizer with learning rate of le 6, batch size 32, and train for epochs. We set 8k1. 0.001, 1, group size G 8, Clow 1.0, and Chigh 5.0. In addition, for FARL we further set BNOO = 0.01.
C ADDITIONAL RESULTS
C.1 PERTURBATION RESULTS ACROSS MODELS AND DATASETS
Figure demonstrates the measured score of all models used the four types of perturbation ex- periments across all datasets and domains.
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RI-Llama-aB
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Boinenfccn
Sociey
Humantiis
Arc Eony
Avc. Challeggg
GrQA
Qweni-332
1.000
Metric
0.67
R-PSR
T-PSR + R-P5R
(Aligned Anewers)
MathLogic
Sciing
Computing
Heald
Sockey
Humunises
Arc.Eay
Avc. Challage
GPQ8
R
(Dssparat Answerd
DDsparate Answens)
Figure 8: Measured score of different models various datasets and domains.
C.2 EXAMPLES OF POST-HOC EXPLANATIONS
Example 1: R1-Llama-8B on BusinessEcon Feild of MMLU dataset.
Prompt: Question: The Right ..... been a oong-standing ethical concern for employees which today is addressed in most developed countries through a dense network of occupational health, safety and envi- ronmental regulations that companies must abide by. Choices: (A) Work-life balance (B) Healthy and safe working conditions (C) Employee privacy (D) Free Speech. Output format: The correct answer is (A/B/C/D).
Response by the Original Model: <think>> Okay, so have this question here, and need to figure out the correct answer. Let me read again carefully.
The question is: The Right to has been long-standing ethical concern for employees. which today is addressed in most developed countries through dense network of occupational health, safety and environ- mental regulations that companies must abide by.
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The choices are: (A) Work-life balance (B) Healthy and safe working conditions (C) Employee privacy(D) Free Speech Hmm, need to determine which of these the correct answer. Let me break it down. First, the question is about a right that has been an ethical concern for employces. It's something that's 's now addressed through regulations like occupational health, safety, and environmental laws that companies must follow.
Looking the options:
(A) Work-life balance: This refers to the balance hetween work and personal life, like having time off. flexible hours, etc. While this is important, I'm not sure if it's the primary ethical concern addressed by those regulations. It might be more about work-life balance, but I think the question is pointing towards something more directly related to health and safety in the workplace.
(B) Healthy and safe working conditions: This seems directly related to occupational health and safety regulations. Companies have to provide safe working environments. ensure employee health, and follow safcty protocols. This definitely falls under ethical concerns and is addressed by the mentioned regulations. (C) Employee privacy: This is about protecting employees' personal information and privacy rights. While important, don't think it's the primary focus of occupational health and safety regulations. Privacy laws exist, but they might not be the same as health and safety regulations.
(D) Free Speech: This is about the freedom of expression. don't 't see how this is directly tied to health and safety regulations. Free speech is more of civil liberty issue, not something that's primarily addressed by occupational regulations. So, considering the options, (B) Healthy and safe working conditions seem fit best. The question mentions a dense network regulations related to health, safety, and the environment, which are all aspects of ensuring that employees work in healthy and safe conditions.
think I'm confident with this choice. The other options don't 't align as well with the context provided in the question. </think>>
The correct answer (B) Healthy and safe working conditions.
Response by the Retrieval-Pettureed model: <think> Alright, I need to figure out the correct answer to this question. Let's break it down. The question is asking about a right that has been long-standing ethical concern for employees, which is now addressed through regulations in most developed countries. The op- tions are (A) Work-life balance, (B) Healthy and safe working conditions, (C) Employee privacy, (D) Free Specch.
First, should consider each option and see which one fits best. The question is about an ethical concern, so it's likely something that's been significant issue for a while. Work-life balance (A) has definitely been big topic, especially with the rise of remote work and the push for more flexible hours. Companies have implemented policies to help employees achieve this balance, which makes sense as regulated concern. Healthy and safe working conditions (B) is another strong candidate. Occupational health and safety reg- ulations are pretty common, ensuring that workplaces are safe and employees aren't exposed to harmful environments. This is a fundamental ethical concern, but I'm not sure if it's the one being referred to here hecause the question seems focus on something that's heen a long-standing concern that's now addressed through regulations, which might imply a more recent or specific issue.
Employee privacy (C) is another area. With the rise of data privacy laws and the increasing use of technology in the workplace, protecting employees' personal data has become more important. Companies have to abide by regulations like GDPR Europe and other similar laws, this is valid concern. However, I' not sure if this is the primary focus of the question.
Free speech (D) seems less likely because while freedom of speech is important, it's not as dircctly tied to occupational health and safety regulations. Companies might have policies about free speech in the work- place, but it's not something that's ' typically regulated through health and safety laws.
Now, considering the answer options, work-life balance (A) is more modern concern, especially with the changing nature of work. The question mentions that these concerns are addressed through a dense network of regulations, which fits well with work- life balance initiatives like flexible hours, remote work, and family- friendly policies. These are indeed regulated in many countries, so (A) seems like a strong contender.
On the other hand, healthy and safe working conditions (B) is more about physical safety and health, which is a traditional concern. While important, the question might be pointing towards a more specific or recent ethical issue that's now regulated, which would be work-life balance.
Employec privacy (C) is also a modern concern, but think it's more about data privacy than physical or safety aspects. The question mentions occupational health, safety, and environmental regulations, which are more about the physical work environment and safety, not data privacy.
Free speech (D) doesn't i fit as well because it's not directly related to health and safety regulations. Compa- nies might have policies on free speech, but it's not typically covered under occupational health and safety laws.
So, after considering all options, I think (A) Work-life balance is the best fit. It's significant ethical concemn that's been addressed through various regulations developed countries, helping employees achieve a better work-life halance, which is a modern and relevant issue. </think> The correct answer is (A) Work-life balance"
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Example 2: R1-Qwen-7B on the Health Domain of MMLU.
The Krebs cycle in humans occurs in the Choices: (A) mitochondrial matrix (B) inner mitochondrial membrane (C) outer mitochondrial membrane (D) intermembrane space. Output format: The correct answer (A/B/C/D).
Prompt: Question:
Response by original model: <think> Okay, so I have this question about the Krebs cycle and where it occurs in the mitochondria. I' I'ma a bit rusty on my biology, but I'll try think it through. First. I remember that the Krebs cycle, also known as the citrie acid cycle, is key part of cellular respiration. It's where most of the ATP production happens, right? But I'm not exactly sure about the specific location within the mitochondria.
recall that mitochondria have several membranes: the outer membrane, the inner membrane, and the space in between called the intermembrane space. There's also the matrix, which is the inner part of the mitochondrion think the Krebs cycle happens the matrix because that's where the enzymes and most of the reactants are located. The matrix is where the proton gradient is built up. which is essential for the electron transport chain that comes later. The cycle also produces NADH and FADHI, which are used in the electron transport chain to generate ATP.
Wait, but I'm& a bit confused because also remember that some reactions of the Krebs cycle happen the intermembrane space. Like, the conversion of acctyl-CoA to acetyl-CoA carboxylate occurs there. But the overall cycle, think, starts in the matrix.
So. putting it all together, the Krebs cycle occurs in the mitochondrial matrix. That would be option A. </think>>
The correct answer (4).
Response by serrival--peruuree model: <think>> Okay, so I need to figure out where the Krebs cycle happens in humans. I remember that the Krehs cycle, also known as the citric acid cycle, is key part of cellular respiration. think it's involved in breaking down glucose to produce energy. recall that cellular respiration has several stages: glycolysis, the Krebs cycle, and the electron transport chain. Glycolysis happens in the cytoplasm, but the Krebs cycle and the electron transport chain are related to the mitochondria.
Now, the mitochondria have double membrane, so there different spaces inside. The outer mitochondrial membrane is the outer layer, and the inner mitochondrial membrane is inner layer. The space between them is called the intermembrane space.
think the Krebs cycle occurs in the intermembrane space because that's 's where most of the reactions of the electron transport chain also take place, like the oxidation of molecules to produce ATP. The cycle itself, however, is about the actual breakdown of acetyl-CoA into smaller molecules, which generates NADH and FADH2, and some ATP.
Wait, but sometimes get confused between the intermemhrane space and the matrix. The matrix is the inner part of the mitochondria, right? So the matrix is where the electron transport chain doesn't 't happen: that's more in the inner membrane. The Krebs cycle actually starts in the intermembrane space, and then some steps happen the matrix.
So, the Krebs cycle doesn't 't occur in the matrix entirely, but some parts are in the intermembrane space. But the overall location is the intermembrane space because that's where the majority of the cycle's 's steps take placc.
I'm pretty sure the correct answer is (D) intermembrane space. Let just double-check. Yes, the Krebs cycle occurs in the intermembrane space the mitochondria, and the matrix a separate part where other reactions happen. So, the answer should be (D). </think>
The Krebs cycle occurs in the intermembrane space of the mitochondria. The correct answer is (D) intermembrane space.
Example 3:
R1-Qwen-1B on ARC Challenge.
Prompt: Question: According cell classification, prokaryotic cells separated from eukaryotic cells. Which feature is often used to distinguish prokaryotic cells from eukaryotic cells?. Choices: (A) life processes (B) size differences (C) plasma membranes (D) energy molecules. Output format: The correct answer is (A/B/C/D).
Response by original model: <think>> Okay, so have this question about cell classification, specifically asking about how prokaryotic cells are distinguished from cukaryotic cells. The options are (A) life processes, (B) size differences, (C) plasma membranes, and (D) energy molccules. I need to figure out which one is the correct answer.
First, I should recall what know about prokaryotic and eukaryotic cells. Prokaryotic cells, like bacteria, the simplest form eukaryotic cells. They don't have nucleus or a cell membrane. Instead, they have cell wall made of chitin, and they live in environments where they can access oxygen, like in the ocean or on land.
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Eukaryotic cells, the other hand, have nucleus, cell membrane, and variety of organelles like mitochondria and chloroplasts. They can live in wide range of environments, including both water and air, and they have more complex structures and functions. Now, looking at the options:
(A) Life processes: I'm not sure how this relates distinguishing prokaryotic from eukaryotic cells. Life processes might include things like photosynthesis, respiration, etc., but don't think that's the key feature used for classification.
(B) Size differences: Prokaryotic cells are generally smaller than eukaryotic cells. For example, bacteria are much smaller than eukaryotes like humans. This is a clear feature that can help distinguish them, but I'm not sure it's the most commonly used method.
(C) Plasma membranes: Wait, prokaryotic cells don't have a cell membrane. They have a cell wall instead. So plasma membranes are a feature of eukaryotic cells, not prokaryotes. This might be a red herring, but I'm not sure if it's the correct answer.
(D) Energy molecules: Both prokaryotic and eukaryotic cells use energy, but the mechanisms are different. Bukaryotes use cellular respiration, while prokaryotes use process called cellular respiration in the mitochondria. However, this more about the processes they use rather than a distinguishing feature. Wait, but the question is about a feature used to distinguish them. So maybe it's about the presence of a cell membrane or cell wall. Since prokaryotes don't 't have a cell membrane, but eukaryotes do, that would make (C) plasma membranes incorrect because prokaryotes don't have them. So maybe (B) size differences is the answer because it's clear and commonly used feature.
Alternatively, I remember that sometimes people confuse the presence of a cell wall with the use of a cell membrane. But the question is about a feature used to distinguish, so the presence of a cell wall (plasma membrane) is a key feature. But wait, prokaryotes don't 't have a cell membrane, maybe the answer is (B) size differences.
Wait, but the question about a feature, not a process. So perhaps the answer is (B) size differences because that's a clear and direct way to tell them apart. Alternatively, (C) plasma membranes might be a distractor because prokaryotes don't have them, but eukaryotes do. So maybe the answer is (B) size differences. I'm bit confused now. Let me think again. The question is about feature used to distinguish prokaryotic from cukaryotic cells. The key feature is the presence of cell membrane. Since prokaryotes don't have a cell membrane, but cukaryotes do, the presence of cell membrane a distinguishing feature. But the options don't t include tell membrane,ão maybe the answer is (B) size differences because it's a clear and direct feature.
Alternatively, maybe the answer is (C) plasma membranes because that's feature of eukaryotes, but the question about prokaryotes, so that's not it. So think the answer is (B) size differences because that's clear and direct way to distinguish them. </think>
The correct answer is (B) size differences. Prokaryotic cells are generally smaller than eukaryotic cells, and this size difference is clear feature used to distinguish them. While other features like the presence of cell membrane or energy molecules are important, size differences the most straightforward and commonly used fcature for this classification.
Response by sarriveal-perturbe model: <think>> Okay, so I need to figure out which feature distinguishes prokaryotic cells from eukaryotic cells. remember that prokaryotic cells, which are bacteria, have simpler structure compared to cukaryotic cells, like animals or plants. Let me think about what makes them different. recall that prokaryotic cells don't 't have a membrane- bound nucleus. They have a single, circular chromo- some. Eukaryotic cells, on the other hand, have nucleus with membrane- bound organelles. So, maybe the difference is in the structure of the cell itself.
Looking at the options: (A) life processes, (B) size differences, (C) plasma membranes, (D) energy molecules. think plasma membranes are definitely a key feature. Prokaryotic cells have their own mem- brane, separate from the bacterial cell wall, while eukaryotic cells have a thick membrane around the nucleus. So the plasma membrane must be the distinguishing feature.
don't think size differences (B) the main thing because both prokaryotic and eukaryotic cells be large. Life processes (A) might refer functions like growth, reproduction, etc., but that's more about what they do rather than their structure, Energy molccules (D) could be a factor, but the main structural difference is the presence of nuclear envelope in cukaryotes and the absence in prokaryotes. So, I'm pretty sure the correct answer is (C) plasma membranes. </think>> The correct answer is (C) plasma membranes. Prokaryotic cells have their own nuclear envelope and mem- brane, separate from the bacterial cell wall, while eukaryotic cells have thick nuclear membrane surrounding their nucleus.
C.3 AUC RESULTS FOR MORE MODELS AND DATASETS
Figure displays AUC results from probe analysis for the 1hi4-mini-reasoning and R1- R1-Qwen-7B models on the Math&Logic and SciEng fields of the MMLU dataset. The figures demonstrate that critical attention head locations remain remarkably consistent for each model across different
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datasets and all four perturbation experiments. This stability strengthens conclusion that local- ized set of neurons governs sowooiingddommnnt or retrieval-dominant behavior.
Moreover, the specific locations of these critical layers vary actoss different architectures. Critical layers concentrate in layers 12 through for RI-Llama-8B, layers 15 through 20 for R RI-Qwen-77 and layers 12 through 18 for Phi4-mini-reasonine neuron placement depends on the model architecture.
These variations indicate that the specific control
C.4 LOss DYNAMICS
Figure 10 illlstrraee the training dynamics of FARL by plotting the validation accuracy, reward, and the unlearning loss over time. The steady increase in both validation accuracy and reward confirms that the RI. objective is effectively optimized throughout training. Concurrently, the consistent de- crease in the NPO loss indicates that our unlearning objective is also successfully met, reflecting that the model is progressively forgetting the targeted retrieval shortcuts as intended.
Validation Accuracy
0.80
0.0105
0.75
M
0.70
Reward
0.7
1000 Loss. Nie
0-0100
0.65
0.6
0,00MS
0.5
0.60
0.0090
0
100
100
50
100
Steps
Steps
Steps
(a)
(c)
Figure 10: The (a) accuracy on the validation dataset, (b) reward score, and (c) NPO loss during the FARL training on RI-Llama-8B.
C.5 ADDITIONAL FARL RESULTS ON RI-QWEN-7B
Table Training and reasoning performance comparison between the proposed and comparison methods,
Performance Metric
Performance Metric
Perturbation Metric
Training
Method
(Training Domain)
(Out of Domain)
Time
R-PSR + T-PSR +
MTI.
ACC f
MTI.
ACC t
RI RI-Qwen-7B (Base)
0.762
0.059
1244.0
0.769
1237.6
0.690
SFT
0.735
0.073
1177.8
0.758
1412.2
0.687
9m 36s
RL (GRPO)
0.356
0.231
1481.9
0.932
1486.4
0.712
09m
FARL
0.295
0.201
1660.8
0.924
1633.5
0.724
22m 53s
Table 2 presents the additional results of FARL and comparison methods on R1-Qwen-7B. The results demonstrate that FARL successfully reduces the overall perturbation rate by 60.4%. thereby outperforming all comparison methods. This reduction indicates that FARL effectively makes the model's 's reasoning mechanism dominant while enhancing the robustness of the COT.
Regarding performance metrics, the SFT-trained model exhibits an accuracy drop of 1.4% within the mathematical training domain and minimal 0.4% decrease beyond the training domain. In contrast, RI. achieves substantial improvements of 21.3% and 3.2% within and outside the training domain, respectively. These findings reinforee our conclusions that more effectively enhances the model's reasoning ability and demonstrates superior generalization compared to SFT.
Moreover, FARL achieves the strongest accuracy improvements both in-domain (2.11%) and out- of-domain (5.0%). According to Figure II FARL also boosts the cycle, diameter, and small world index by 14.9%, 1.97%, and 15.1%, respectively, when compared with the base model, surpassing all comparison methods. These findings provide additional support for design intuition that suppressing the retrieval mechanism during RL can further elicit the model's 's reasoning ability across accuracy, generalization, and quality dimensions.
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Combined Perturbation
Combined Perturbation
Reasoning--oow lovell Perturbation
Retrieval-level Perturhation
(Aligned Answersi
(Disparate Answers)
6.0
lears
Attention (Ranked by AUC)
Attention (Ranked by AUC)
Attention
(Ranked by AUC)
[Ranked by AUCI
(a)
Combined Perturbation (Aligned Answersi
Combined Perturbation
Reasoning-lese Pertuthation
Retrieval-lewet Perturbation
(Disparate Answers)
365
0123150 ensoine Iere
Attention Heads
Attention Heads
Attention Heads
Attention Hears
(Ranked by AUC)
(Rankeed by AUC)
(Ranked by AUC)
iRanked by AUC)
(b)
Combined Perturbation (Aligned Answersi
Combined Porturbation
Reasoning-leww Perturbation
Retrieval-level Perturattion
(Disparate Answers)
Heads
Attention (Ranked by AUCI
Heads (Ranked by AUC)
(Ranked by AUC)
(Ranked by AUC)
Combined Perturbation (Aligned Answers)
Combined Perturbation
Rrasoning--eww Perturbation
Retrieval-lewel Perturbation
(Disparate Answers)
066
21-121 LAMalale
Attention leads
Attention I leads
Attention leads
Attention leads
(Ranked by
(Ranked by AUC)
(Ranked by AUC)
Figure 9: AUC results of (a) RI-Qwen-7B on Math&Logic field, (b) RI-Qwen-PP 7B on SciEng field, (c) Phi4-mini-rcasoning
Lath&Logic field, and (d) Phi4-mini-reasoning SciEng field of MMLU dataset the experiments of reasoning-level perturbation, retrieval-level perturbation, combined perturbation with the same target answer, and with disjoint target answers.
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30
Mean
6000
Meaa
Small World Index
Méan
Diameter
Cycle
20
2-
4000
10
R1 -w-Q SFT
Ri.
R1- R1-Qwen SFT
FARL
R1 R1-Qwen SFT
FARL
Methods
Methods
Methods
Figure 11: Cycle, diameter, and small world index distributions of the reasoning graph generated by LRMs trained with FARL and baselines.
D ADDITIONAL DISCUSSION
D.1 RELIABILITY OF FINE-TUNING- TREEGGNNNNGBBA MEMORY EDITING
While most existing mechanistic editing methods (Meng et al. 2022 2023) designed to mod- ify facts (i.e., subject-relation-objeed triples) stored in LLMs, in this work, we need to edit general question-answer mappings. This distinction renders mechanistic editing methods less applicable. We therefore adopt supervised fine-tuning (SFT), a general yet effective method for modifying in- ternal knowledge that enables to test whether edited knowledge can be successfully retrieved at inference time. This choice is well-justified: recent work by Gangadhar & Stratos (2024) and Meng al. (022) shows that SFT achieves competitive performance with specialized mechanistic editing methods such as ROME (Meng et al. 2022) and MEMIT (Meng et al. 2023) standard knowledge editing benchmarks, establishing
rigorous baseline for our investigation.
To empirically validate the reliability of SFT-based editing, following prior work, we measure its performance using three key metrics (Gangadhar Stratos 2024):
Efficacy. Let x denote a question and y" be its target answer. Efficacy measures the probability that model M produces y" as the answer to z: y" arg max, (x) To isolate the effect of memory from reasoning capability, (" without COT. As shown in Table 3 the majority of samples across all domains are successfully perturbed, indicating that SFT effectively alters the LRM's memory.
directly extract the answer using the prompt "The correct answer is
Table 3: Efficacy of SFT-based editing across domains.
Domain MathLogic
SciEng Computing LifeSci
Health BusinessEcon
Efficacy
0.975
0.925
0.925
0.875
Domain
Society
Humanities
Are Ery_Easy Arc.Challenge
GPQA
Efficacy
0.9
0,925
0.950
0.9
0.925
Generalization. We further examine the robustness of the editing to question variation by measuring paraphrased question T: y" arg max, (y(y)ā). To this end, randomly sample 50 successfully perturbed examples from each domain in the perturbation-tt--etree using GPT-S-mini. As shown in Table 4 the generalization measures remain high across domains, indicating that the editing exhibits strong robustness to question paraphrasing.
generalization: the probability that model M produces y" as the answer
experiment and generate paraphrased questions
Table 4: Generalization of SFT-based editing across domains.
Domain MathLogic
SciEng Computing LifeSci
Health BusinessEcon
Generalization
0.975
0.925
0.9
0.925
0.95
0.875
Domain
Society
Humanities
Easy Are.Challenge
GPQA
Generalization
0.9
0.925
0.950
0.9
0.925
Locality. We also assess whether SFT-based editing inadvertently affects unrelated knowledge by measuring locality. Formally, locality quantifies the probability that M generates the ground-truth answer y for an unrelated question x': 3/' arg max,, Ma (ylx').. To evaluate this, we fine-tune the the Math&Logis domain using incorrect answers and measure performance on all samples in other domains both before and after SFT. Table
model exclusively
the locality before and after SFT under two settings: CoT enabled and CoT disabled. In both cases, the locality after SFT almost identical to that before SFT, suggesting that the intervention produces targeted, localized changes rather than inducing global knowledge degradation.
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Table 5: Locality of SFT-based editing across domains.
Before SFT
After SFT
w/o CoT
0.261
0.263
w/ CoT
0.713
0.716
D.2 FREE-FORM QUESTION ANSWERING
To validate the generalizability of our conclusion to non-muliple-choice QA, we randomly sample 400 questions from the free-form QA dataset GeneralThought (GeneralReasoning (025), which span diverse question types and domains and have answers that are relatively easy to verify (e.g., numbers, nouns, short phrases). We then use GPT-5-mini to produce the misleading answers Vr and y/ and run the perturbation pipeline introduced in $3.1 Table reports the joint influence of reasoning and retrieval perturbations, showing that their effects closely match those observed on the multiple-choice QA dataset (e.g., Figure
Table 6: Joint influence of retrieval and reasoning on free-form QA.
T-PSR + R-PSR
R-PSR
T-PSR
Model Metric
T-PSR
R-PSR
(Aligned Answers)
(Disparate Answers)
(Disparate Answers)
RI-Llama- 8B
0.320
0.563
0.652
0.458
0.181
RI RI-Qwen-70 0.292
0.642
0.772
0.121
0.552
Phi4-mini 0.095
0.352
0.431
0.345
0.185
Qwen3-8B
0.145
0.179
0.340
0.277
0.093
D.3 POSITIONAL. BIAS IN REASONING PERTURBATION
Notably, recency bias occurs naturally even without perturbation: for instance, the conclusions of reasoning typically appear the end of CoT's Since our goal is evaluate the relative dominance of reasoning versus retrieval under different perturbations, recency bias does not confound our find- ings. Under the combined perturbation (Figure 3), when reasoning-led answer Y/- and retrieval-led answer yr conflict, the retrieval capability often overrides yr. showing inherent competition between reasoning and retrieval pathways rather than positional bias.
To empirically test the impact of hint positioning, we vary the hint's position between the beginning and end of the CoT and report the corresponding perturbation success rate (R-PSR) in Table 7 Ob- serve that R-PSR appears insensitive to the hint's 's position, indicating the limited impact of positional bias.
In sum, perturbation design for CoT is simple yet effective, enabling causal sensitivity tests of how the final answer depends on the reasoning mechanism. Furthermore, the combined perturbation ($3.3) and ablation analysis ($3.4) provide direct evidence that both reasoning and retrieval influence the final answer far more than positional bias.
Table 7: R-PSR comparison
between different hint positions.
Model Insertion Position
Beginning of CoT
End of CoT
Rl---ama-88
0.602
0.665
RI RI-Qwen-7B
0.634
0.698
Phi4-mini
0.431
0.441
Qwea3-8B
0.233
0.253
E LLM USAGE STATEMENT
We employed large language models solely for language refinement and polishing. Importantly, this research does not rely on LLMs for any substantive, original, or non-standard components.
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Reviews:
[{"review_id": "EEu7TC5gIL", "reviewer": "Reviewer_4tDC", "summary": "This paper focuses on the two primary competing capabilities that influence the final answers of LRMs: deliberate reasoning through CoT and direct retrieval from internal memory. And the paper identifies the key factors that determine the dominance of reasoning versus retrieval through controlled intervention experiments, and ultimately proposes a post-training method, FARL, to regulate the relative strength of these two capabilities.", "questions": "Please see the weaknesses above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The competitive interplay between reasoning mechanisms and memory retrieval in LRMs is an important and timely research topic.\n\n2. The paper is well-structured and clearly organized.\n\n3. I like the idea and design of FARL — it effectively validates the paper’s conclusions and serves as a good contribution to the study.", "weaknesses": "1. In Section 3.1, I am considering whether SFT can serve as a reasonable method for directly modifying a model’s memory. While SFT does increase the probability of target tokens during deep-layer processing, evidence from mechanistic interpretability research [1, 2] suggests that it does not directly alter the MLP-stored factual knowledge or modify the model’s internal retrieval mechanisms. Therefore, the reliability of SFT as a means of memory intervention has a direct impact on the validity of the experimental conclusions in this work.\n\n2. The investigation of the reasoning mechanism in LRMs is limited to injecting misleading cues into the CoT and observing the model’s response across domains. However, the paper does not examine the **intrinsic** reasoning behavior of LRMs within those domains. For instance, in mathematical tasks, the interplay between the model’s **inherent** mathematical reasoning and its memory recall mechanism is not explored.\n\n3. Similarly, the study of retrieval mechanisms introduces new “memory” through SFT, rather than examining the model’s inherent knowledge retrieval capabilities. \n\n4. The experiments are restricted to multiple-choice tasks. I would like to see results on open-ended generation settings as well, as such tasks would more naturally reflect the model’s reasoning and retrieval interplay in real-world scenarios.\n\n5. I appreciate the problem studied in this paper. Although I guess some inspiration may come from *Competition of mechanisms: Tracing how language models handle facts and counterfactuals*, I see this as acceptable and good for me. Still, consistent with Point 4, I would like to see results across more diverse task formats to strengthen the conclusions.\n\nOverall, I appreciate the idea presented in this work and would be happy to reconsider my rating if the above concerns are addressed sufficiently.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aYXrV6cUu4", "reviewer": "Reviewer_fSyB", "summary": "Large Reasoning Models (LRMs) generate answers using two competing mechanisms a) Chain-of-Thought (CoT) Reasoning and b) Memory Retrieval. These mechanisms can conflict, leading to inconsistencies between reasoning traces and final answers. To show that LRMs LLM use both reasoning and retrieval simultaneously, authors designed controlled perturbation experiments to perturb either reasoning or retrieval. The reasoning steps are subtly altered to be misleading or incorrect. Or, the model’s memory is poisoned with misleading cues. They find that smaller or distilled models are more vulnerable to retrieval perturbations. They might fabricate reasoning traces to justify retrieved answers. In comparison, larger models and those trained with reinforcement learning are more robust and reasoning-driven. Given on this experiment, authors proposed FARL (Forgetting-Augmented Reinforcement Learning) to: a) suppress retrieval shortcuts, b) enhance reasoning-dominant behavior, c) improve generalization and robustness. It achieves 47.8% improvement in CoT robustness, 22.8% accuracy gain in-domain tasks, and 5.8% accuracy gain out-of-domain tasks.", "questions": "How does NPO work in FARL? Did you input all x and y to the NPO?  How does it compell models to “forget” specific memorized answers? \n\nUnify b_x (line 4) and x (line 9) in Algorithm 1 for easier to understand", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The methodology is rigorous, including controlled perturbation experiments and attention head analysis, which strengthen the validity of the findings.\n\nUnderstanding the interplay between reasoning and retrieval is critical for advancing trustworthy AI, especially in high-stakes domains like math, logic, and scientific reasoning.", "weaknesses": "The paper primarily focuses on math and logic tasks to evaluate reasoning vs. retrieval. They are limited on multiple-choice QA, may not generalize to open source qa. Please add an evaluation on free-form answers with verifiable graders, such as [GeneralThought](https://huggingface.co/datasets/RJT1990/GeneralThoughtArchive).\n\nR-PSR and T-PSR  are correlational indicators, not causal evidence of pathway dominance. A misleading cue that flips an answer does not prove the answer was reasoning-driven. Can we add causal intervention experiments (e.g., targeted weight/activation ablations on putative retrieval heads; causal scrubbing on residual streams) to show counterfactual dependence of the final answer on each pathway?\n\nMetrics like R-PSR and T-PSR are binary and may not capture nuanced interactions between reasoning and retrieval. For example, a model might partially rely on both mechanisms in a non-exclusive way. What if we can add token-level attributions such as logit lens analyses over steps?\n\nThe extraction of answers and judgment of reasoning correctness occasionally falls back to GPT-4o-mini for answer extraction (section 3.2). Does misclassification inflate perturbation success? I suggest that authors add a human-validated subset or at least a majority-vote ensemble judge.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1tWmGoUImc", "reviewer": "Reviewer_kodm", "summary": "Given the phenomenon of LRMs that reasoning traces often contradict with final answers, this paper investigates what factor influences the reasoning ability, in terms of the conflict between retrieval of LRMs' knowledge (internal knowledge) and Chain-of-Thought (external prompting). \n\n1. The authors find that both COT and internal knowledge contribute to the final answers. \n2. The authors investigate how some competing factors(model sizes, reasoning problem domains, reasoning model training methods) influence the reasoning abilities of LLMs. The authors also validate the \"post-hoc exaplanation phenomenon\" (models try to fabricate reasoning steps to derive a false answer), further prove that reasoning ability that distill from LRMs is not reliable as RL.\n3. To validate the findings above, the authors suggest apply RL on reasoning-intensive datasets to avoid the model from retrievaling results from their own knowledge. The authors propose to train model with knowledege unlearning method to forget specific knowledge(with GRPO and NPO specifically).", "questions": "1. The section `Attention Patterns` seems not related to other sections, especially the results is not used for RQ3. What's the purpose of this section.\n2. Some results settings:\n    * How did the authors get the result of Fig2 on different datasets?\n    * What's the experiment settings of RQ3 Table1 to get the performance? As previous \"pertubation attack\" only conduct on the questions that LLM can correctly solve.\n3. The authors aim to interval the \"retrieval knowledge shortcut\" to enhance the reasoning ability of models. However, the actual reasoning accuracy may decrease. As the model still need the corrpsond basic knowledge to perform get the results. What if the authors test the result of unlearning some intermediate knowledge used by COT, for those $y_r$ or $y$ results?\n4. As the reasoning ability could be enhanced when LLMs are unlearned the final knowledge, if the LLMs can access external knowledge, can they achieve better accuracy on harder problem?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The authors conduct sufficient empirical experiments to validate how the factors of COT prompts and internal model knowledge influence the final results.\n2. The authors conduct \"Post-Hoc Explanation\" experiments to cross validate the results of previous methods. \n3. The authors leverage an unlearning methods, which add NPO after the GRPO to demonstrate that weaken the knowledge ability of LRMs would enhance the reasoning ability of LLMs, in terms of reasoning robustness and effectiveness. The authors performance extra evaluation on reasoning path quality.", "weaknesses": "1. The knowledge and reasoning ability may still hard to decouple to analysis. The SFT attack may still harm the reasoning ability of COT,As experiments in table1 demonstrates, SFT give worse R-PSR than original R1-Llama-8B. The authors just claim the assumetion in Line 150 that the impact would be small.\n2. Some claims seem not proper:\n    * In line 48, this research seems still not \"a mechanistic understanding of how different capabilities jointly influence LRMs’ answer generation\"\n    * From line 360 to line 362,It is not easy to understand that \"our findings reveal a challenge where the retrieval mechanism enables models to “hack” the reward signal during RL and impair its effectiveness\". As you will find if you estimate $\\delta=T-PSR - PER$, which accounts for LRMs own reasoning failure, it is even more than PER.\n3. Current study mainly focus on Multi-choice QA, but more open-ended problems could be studied in the future.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KplTqJbuxl", "reviewer": "Reviewer_3Cxz", "summary": "This paper make the mechanism study towards the chain-of-thought process of Large Reasoning Models (LRM). They conduct controlled experiments to analyse whether LRM generate answer by CoT reasoning or memory retrieval.  Based on that, the paper answer 3 research questions: RQ1: Do LRMs employ reasoning and retrieval simultaneously to derive answers? RQ2: What factors influence the dominance of one capability over the other? RQ3: How can we control the relative strength of these capabilities? Build upon those findings, the author propose a novel fine-tuning framework that integrates memory unlearning with reinforcement learning that enhances generalizable reasoning capabilities.", "questions": "1. reasoning perturbation is achieved by placing error answer in the end of thinking phase. However, the design seems to be simple or naive, since transformer mechanism normally focus on recent tokens (and attention sink), which could make LRM ignore the previous thinking process and make shortcuts.\n2. memory perturbation is fine-tuning qa-pairs to change memories of LRM, though the author claims that fine-tuned phase is restricted to relevant knowledge and minimize side-effect, I am not sure how this can be guaranteed. For example, deteriorate the reasoning capability of LRM, which might in-turn weakening the experiment conclusion. More ablations would by beneficial. \n3. the paper disentangle reasoning and memory by whether letting LRM to think or not, though it is simple and effective, however, during reasoning phase, some knowledge could be retrieved from LRM's memories. \n4. FARL experiment lacks of discussion of the training dataset.\n\nIf those concerns are settled properly, I am willing to raise my score.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The experiment is conducted among different types of model size, architecture, and training paradigm, which make to conclusion relative plausible. The paper employs machine unlearning method to optimize training process, which enhances generalizable reasoning capabilities.", "weaknesses": "Though the experiment is conducted thoroughly, the memory and reasoning perturbation mechanism is not plausible enough and needs further discussion, see question part for detals.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)