Unraveling the Roots of AI Reasoning

Author: Denis Avetisyan

New research sheds light on how distilled AI models learn, pinpointing the origins of their decision-making processes.

Reasoning Distillation Provenance Tracing was applied to the LIMO-v2 model across AIME24 and GPQA-D benchmarks, revealing how varying teacher models influence the probability of selecting specific actions at each step - as demonstrated by the analysis of action probabilities indexed on the x-axis and quantified on the y-axis. — Reasoning Distillation Provenance Tracing was applied to the LIMO-v2 model across AIME24 and GPQA-D benchmarks, revealing how varying teacher models influence the probability of selecting specific actions at each step – as demonstrated by the analysis of action probabilities indexed on the x-axis and quantified on the y-axis.

A novel provenance tracing method reveals the contribution of teacher models versus inherent capabilities in reasoning distillation, enabling improved data selection and model alignment.

Despite the growing success of reasoning distillation in transferring capabilities from large to smaller language models, a critical gap remains in understanding where a distilled model’s reasoning truly originates. This work, titled ‘Where Did This Sentence Come From? Tracing Provenance in LLM Reasoning Distillation’, introduces a novel framework for tracing the provenance of each action taken by a distilled model, revealing the extent to which its behavior stems from the teacher or its pre-existing knowledge. Our analysis demonstrates that distilled models can generate teacher-originated reasoning during test time, and further enables a data selection strategy that prioritizes these actions to improve generalization. Could a deeper understanding of this provenance unlock more effective and reliable knowledge transfer in future language models?

The Fragility of Thought: Reasoning Limits in Large Language Models

Although large language models demonstrate remarkable proficiency in tasks like text generation and translation, their ability to perform complex reasoning remains surprisingly fragile. These models often succeed in narrow, well-defined scenarios but falter when confronted with novel situations or problems requiring multi-step inference. This ‘brittleness’ manifests as inconsistent performance; slight alterations to the problem’s phrasing can lead to drastically different – and often incorrect – answers. Furthermore, a lack of systematicity is apparent, as models frequently fail to apply logical principles consistently, instead relying on statistical correlations learned from vast datasets rather than genuine understanding. This limitation suggests that scaling up model size alone is insufficient; true progress hinges on developing architectures and training methods that foster robust, reliable reasoning capabilities.

The pursuit of increasingly powerful large language models has, to a certain extent, hit a wall. While boosting model size-the number of parameters-initially yielded performance gains, research demonstrates diminishing returns. Simply making models larger primarily enhances their memorization capacity – the ability to recall patterns from vast datasets – but does little to fundamentally improve their capacity for genuine reasoning. This suggests that progress hinges not on brute-force scaling, but on developing novel architectures and training techniques that actively cultivate reasoning ability. Future advancements will likely prioritize methods that enable models to generalize from learned examples, apply logical principles, and effectively navigate complex problem spaces – skills distinct from mere pattern recognition and rote learning.

The capacity of large language models to sustain logical consistency over extended outputs remains a significant hurdle in their development. While adept at short-form responses, generating long, coherent texts – such as detailed reports, complex narratives, or comprehensive explanations – frequently exposes flaws in their reasoning. These models often exhibit a tendency to drift from the initial premise, introduce internal contradictions, or fabricate details as the response length increases, demonstrating that scaling parameters alone does not guarantee sustained logical flow. This limitation is particularly critical because many advanced applications-including automated content creation, in-depth research assistance, and sophisticated chatbot interactions-depend fundamentally on the ability to produce lengthy, reliable, and logically sound outputs.

Reasoning distillation aims to transfer the teacher model's behavior to a student model during training, but ensuring continued alignment-rather than reversion to student-only behavior-remains a key challenge for generalization at test time. — Reasoning distillation aims to transfer the teacher model’s behavior to a student model during training, but ensuring continued alignment-rather than reversion to student-only behavior-remains a key challenge for generalization at test time.

Reasoning Distillation: Imparting the Power of Thought

Reasoning Distillation represents an advancement in model training by utilizing the capabilities of large, established models – specifically DeepSeek-R1 and QwQ-32B – to improve the performance of smaller student models. This technique transfers knowledge not simply through mimicking outputs, but by guiding the student model to replicate the reasoning processes of the larger teacher model. This approach allows for the creation of more efficient and compact models capable of achieving performance levels previously requiring significantly greater computational resources, offering a pathway to deploy complex reasoning abilities on resource-constrained platforms.

Traditional knowledge distillation transfers information from a large, pre-trained “teacher” model to a smaller “student” model by minimizing the difference between their output predictions. Reasoning distillation diverges from this approach by prioritizing the transfer of the teacher model’s reasoning process. This is achieved by training the student model not only to replicate the correct answer, but also to mimic the intermediate reasoning steps – such as the generation of supporting statements or the selection of relevant evidence – that led the teacher model to its conclusion. This focus on the ‘how’ rather than the ‘what’ aims to equip student models with enhanced reasoning capabilities beyond simply pattern matching, allowing them to generalize more effectively to novel situations.

Training of student models in reasoning distillation utilizes datasets such as OpenThoughts, which are specifically designed to capture complex reasoning processes. OpenThoughts comprises a large collection of human-generated thought chains – detailed, step-by-step explanations for arriving at answers – paired with corresponding questions. This data format moves beyond simple input-output examples and provides the student model with explicit demonstrations of how to reason, enabling it to learn not just the correct answers but also the underlying logic and strategies. The dataset’s scale and nuanced reasoning examples are crucial for effectively transferring capabilities from larger teacher models to smaller, more efficient student models.

Reasoning Distillation Provenance Tracing reveals that the probability of selecting specific actions varies with action index for both Deepseek-Distill-Qwen-7B and DeepSeek-R1-0528-Qwen3-8B models when evaluated on the AIME24 and GPQA-D reasoning benchmarks.

Provenance Tracing: Deconstructing the Reasoning Pathway

Reasoning Distillation Provenance Tracing analyzes the origins of each action taken by a student model during reasoning. This is achieved by categorizing generated sentences into four types: teacher-originated, representing direct copies from the teacher model; student-originated, indicating novel content generated by the student; shared, denoting sentences present in both teacher and student outputs; and boosted, identifying instances where the student model amplifies or elaborates upon teacher-provided content through distillation. This four-way classification allows researchers to deconstruct the reasoning process and determine the extent to which the student model relies on the teacher versus exhibiting independent reasoning capabilities.

Action Type Classification within Provenance Tracing facilitates granular analysis of a student model’s reasoning process by categorizing each action as originating from the teacher, being generated independently by the student, representing a shared contribution, or being a boosted action derived from distillation. This classification allows researchers to determine at which specific reasoning steps the student model defers to the teacher’s knowledge, and conversely, where it demonstrates autonomous reasoning capabilities. By isolating these distinct action types, the methodology enables precise identification of the student model’s reliance on, and divergence from, the teacher’s guidance throughout the reasoning chain, providing insights into the effectiveness of the distillation process and the student’s learning trajectory.

Boosted sentences, identified through Provenance Tracing, represent instances where the student model’s actions demonstrate an amplification of information originating from the teacher model during the distillation process. This amplification isn’t simply replication; it indicates the student model has not only adopted a teacher-generated sentence but has also increased its contribution to the final output, suggesting successful knowledge transfer and leveraging of the teacher’s expertise. Quantitatively, boosting is determined by comparing the log-likelihood of a sentence in both the teacher and student models; a significantly higher log-likelihood in the student model, given the teacher’s sentence as input, signals a boosted action. Analysis of these sentences provides insight into specific reasoning steps where distillation is most effective, highlighting areas of successful knowledge acquisition within the student model.

Reasoning Distillation Provenance Tracing visualizes model decision-making by displaying the probability of each action (represented by colored curves) across a response sequence, with background colors indicating action types and segment boundaries highlighted.

Optimizing Distillation Through Strategic Data Selection

Reasoning Distillation, a technique for transferring knowledge from a large “teacher” model to a smaller “student” model, benefits from targeted data selection methods such as GRAPE and Teacher-Guided Data Selection. These methods improve training efficiency and performance by prioritizing examples that are most informative for the student. GRAPE focuses on selecting data points that align with the student model’s current output distribution, effectively concentrating learning on areas where the student is likely to improve. Teacher-Guided Data Selection refines this approach by specifically emphasizing training examples where the student model can learn to replicate the reasoning steps demonstrated by the teacher model, thereby promoting behavioral mimicry and enhancing the distillation process.

Teacher-Guided Data Selection improves upon the GRAPE method by incorporating reasoning step provenance into the data selection process. While GRAPE prioritizes examples based on disagreement with the student model, Teacher-Guided Data Selection further refines this by distinguishing between instances where the student’s error originates from incorrect reasoning steps versus incorrect final answers. This allows the training process to specifically target examples where the student’s reasoning diverges from the teacher model, rather than simply penalizing incorrect outcomes. By focusing on the origin of errors, Teacher-Guided Data Selection provides a more granular and effective approach to identifying informative training examples, ultimately leading to improved student model performance.

Evaluations of Reasoning Distillation techniques, specifically GRAPE and Teacher-Guided Data Selection, consistently show an average accuracy improvement ranging from 1.7% to 2.5% across diverse benchmark datasets. This performance gain is achieved by strategically prioritizing training examples, leading to a more efficient learning process for the student model. The observed improvements indicate that targeted data selection not only enhances the final accuracy of the distilled model, but also reduces the computational resources required for training compared to methods utilizing randomly selected data.

Teacher-guided data selection consistently outperforms the previous method (grape) across all evaluated metrics.

Towards Robust and Explainable Systems: The Pursuit of Transparent AI

Model auditing represents a significant advancement in the field of artificial intelligence, building upon established techniques like reasoning distillation and provenance tracing to offer a holistic understanding of generative models. This approach doesn’t simply assess what an AI produces, but meticulously details how it arrives at a given output, and crucially, what specific data points influenced that process. By effectively reconstructing the model’s ‘line of reasoning’, auditors can pinpoint memorization of training data – a key vulnerability – and identify the precise pathways through the network that led to a particular result. This granular level of insight is transforming the development of AI, enabling researchers to move beyond ‘black box’ systems towards models that are demonstrably reliable, trustworthy, and capable of justifying their conclusions.

The pursuit of robust and reliable artificial intelligence necessitates a deep understanding of a system’s inner workings, especially when deployed in sensitive contexts. Applications spanning healthcare, finance, and criminal justice demand not only accurate predictions but also transparent reasoning, as errors can have significant consequences. Enhanced understanding allows developers to proactively identify and mitigate potential biases, vulnerabilities, and failure modes within AI systems. This proactive approach fosters trust and enables meaningful accountability, ensuring that decisions made by these systems can be scrutinized and justified. Ultimately, a commitment to transparency and explainability is paramount for building AI that is not only powerful but also responsible and aligned with human values, paving the way for wider acceptance and beneficial integration into society.

The pursuit of genuinely intelligent artificial systems extends beyond mere task completion; a critical frontier lies in enabling these systems to articulate why a particular decision was reached. Recent advances focus on tracing the lineage of an AI’s reasoning – identifying the specific data points and computational steps that culminated in a given output. This isn’t simply about reverse-engineering a result, but reconstructing the thought process, revealing the evidence that supported the conclusion. By meticulously mapping these origins, researchers aim to build AI capable of providing transparent justifications, fostering trust and accountability, and ultimately moving beyond ‘black box’ functionality towards systems that can demonstrably explain their rationale – a crucial step for deployment in high-stakes domains like healthcare and legal reasoning.

Reasoning Distillation Provenance Tracing reveals that Deepseek-Distill-Qwen-7B and DeepSeek-R1-0528-Qwen3-8B assign varying probabilities to different actions across reasoning benchmarks like AIME24 and GPQA-D, as visualized by action index on the x-axis and probability on the y-axis.

The pursuit of understanding a distilled model’s reasoning, as detailed in this work, echoes a sentiment deeply held by Carl Friedrich Gauss: “If others would think as hard as I do, they would not have so many questions.” This paper undertakes the difficult task of dissecting where a student model’s responses originate – from the teacher, or from its own internal logic. The provenance tracing method isn’t merely about auditing; it’s about establishing a clear lineage of thought, identifying the true source of each reasoning step. By prioritizing teacher-originated actions during data selection, the research aims for a distillation process free of unnecessary complexity, a clarity of thought aligning with Gauss’s preference for rigorous, unadorned understanding.

The Road Ahead

The pursuit of reasoning distillation, as clarified by this work, reveals less a pathway to artificial general intelligence and more a rigorous accounting problem. Provenance tracing, while elegantly demonstrating the debt a student model owes its teacher, also highlights the persistent opacity within even the ‘distilled’ component. The remaining signal – that not directly attributable to the teacher – isn’t necessarily ingenuity, but simply unaccounted-for influence. A fuller understanding requires not merely identifying the source of actions, but quantifying the cost of those actions – the energetic footprint, so to speak, of each inferential step.

Future iterations will likely focus on minimizing this unaccounted-for cost. Data selection, intelligently prioritizing teacher-originated behavior, is a pragmatic step, but feels akin to rearranging deck chairs. The fundamental limitation remains: distillation, by its very nature, amplifies existing biases and imperfections. True progress demands a method for identifying and subtracting the teacher’s flaws, not merely replicating its successes.

Ultimately, the value of provenance tracing may lie not in building better models, but in building better auditing tools. The field risks becoming consumed by the illusion of explainability, mistaking traceability for understanding. Perhaps the most fruitful avenue for research isn’t to ask where a sentence came from, but why it was uttered in the first place.

Original article: https://arxiv.org/pdf/2512.20908.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/