Current Large Language Model (LLM) training paradigms, while efective at pattern matching and knowledge retrieval, often fall short of replicating the nuanced, adaptive and generalizable reasoning characteristics of human intelligence. We argue that this stems from a fundamental disconnect between the static, data-driven training of LLMs and the dynamic, lifelong learning process inherent to human cognitive development that naturally resolves the stability-plasticity dilemma arising while deploying LLMs in non-stationary environments. Traditional approaches like experience replay or regularization often treat data as static points, ignoring the cognitive structures of learning. To bridge this gap, we introduce a novel robust blueprint for streaming and continual learning (SCL), namely Learn-Master-Teach Tuning (LMT2) , a visionary end-to-end training framework that simulates the complete human 'student-to-teacher' life-cycle. Our paradigm guides the model through a comprehensive developmental trajectory, from a novice learner internalizing a curriculum to a seasoned educator capable of lifelong learning and knowledge synthesis. By situating learning within a holistic, cognitive-inspired framework, we explore two fundamental research questions: Can the deep simulation of a human persona, in this case, a developing academic, act as a proxy for drift detection and active learning in streaming environments? And, does this student-teacher life-cycle ofer a superior training paradigm to resolve the plasticity-stability dilemma compared to traditional continual fine-tuning in LLMs? We present the complete LMT 2 methodology and position it within the landscape of existing SCL training paradigms, arguing that by emulating the human journey of learning, we can unlock new frontiers thereby enabling LLMs to operate in dynamic, streaming data environments.
LLMs have demonstrated remarkable capabilities in a wide range of downstream natural language tasks, largely due to their ability to learn from vast amounts of text data and development life-cycle, which typically unfolds across three canonical stages: vast, self-supervised pre-training on web-scale text corpora; supervised fine-tuning (SFT) on curated instruction-response pairs; and alignment through reinforcement learning, often with human feedback (RLHF). This approach, despite resulting in remarkable success, treats learning as a process of mass data ingestion, focused on statistical pattern recognition rather than a structured, developmental journey. The resulting models, while possessing broad knowledge, are merely “approximate omniscients" that excel at next-token prediction but lack the deep, verifiable expertise and robust reasoning due to de-contextualized learning. This stands in stark contrast to the robust, flexible, and continuously evolving nature of human cognition which is what is truly necessary in in real-world applications as data distributions shift, new vocabulary emerges and factual knowledge evolves.
Streaming Continual Learning (SCL) aims to address this by updating models on the fly. However,
current LLM fine-tuning methods, such as LoRA or full-parameter tuning, are susceptible to catastrophic
forgetting where updating weights for new data, say +1 destroys the representations learned for
data at a previous timestep, say . Existing SCL solutions typically fall into three categories: (1)
regularization-based (e.g., elastic weight consolidation [
RQ 1: Can the deep simulation of a human persona in LLMs, which in this case is of a developing academic, translate to replication of the persona’s capability thereby acting as a proxy for drift detection and active learning in streaming environments? RQ 2: Does a student-teacher life-cycle ofer a superior training paradigm with for LLMs to resolve the plasticity-stability dilemma thereby achieving superior retention and adaptation on non-stationary data streams, compared to traditional continual FT? The major contributions of this paper include • LMT2 (Learn-Master-Teach Tuning), a novel multi-stage training paradigm with an end-to-end framework that instead of treating the model as a static entity to be filled with information, guides it through a complete, simulated human life-cycle of learning and growth, from a student to a teacher (see Fig. 1) • MentorX, a 7B-LMT tuned model trained to be an adaptive educational tutor for K-12 mathematics which addresses RQ 1 and SkolarX, which is another 7B-LMT tuned model achieving comparable performance with SFT baselines with significantly lower training data as a response to RQ 2 Rest of the paper is organized as follows: Section 2 discusses the motivation behind our approach, Section 3 constitutes the relevant literature survey followed by the proposed LMT2 methodology in Section 4 and the corresponding experiments in Section 5. Sections 6, 7 and 8 present the limitations, future research directions and conclusions.
Our primary motivation is to explore the transformative potential of situated learning and cognitive
apprenticeship in the context of SCL for LLMs. Lave and Wenger’s theory of situated learning [
Our second motivation, addressed by RQ 2, is the pursuit of a more efective training SCL paradigm. The student-teacher life-cycle is a powerful engine for learning in humans. The process of learning, being tested, and then having to teach others forces a deeper understanding of the material, promotes self-reflection and encourages the development of more robust mental models while keeping prior information intact. We hypothesize that an LLM that undergoes this same process will develop more generalizable reasoning skills, better error-correction abilities, and a greater capacity for lifelong learning. This is a departure from the current paradigm, which often results in models that are “a mile wide and an inch deep."
Streaming Continual Learning: For LLMs, SCL has emerged as a critical research area to address
the challenge of updating pre-trained models on non-stationary data streams while mitigating
catastrophic forgetting, a central issue for deployment in dynamic environments. Recent comprehensive
surveys have highlighted multi-stage categorization schemes like continual pre-training, continual
instruction tuning, and continual alignment as foundational mechanisms for lifelong adaptation and
retention of knowledge over time [
LLM-Based Data Augmentation: Data augmentation techniques for LLM post-training have become essential for improving model performance, especially when labeled data is scarce or diverse data is needed. Common strategies include prompt-based augmentation, where LLMs generate new training examples by rephrasing, paraphrasing, or expanding existing data using carefully designed prompts, and retrieval-based augmentation, which incorporates external knowledge to produce more grounded and contextually rich data. Hybrid approaches combine these methods to maximize both diversity and faithfulness of the generated samples.
Multi-Stage LLM Post-Training Paradigms: Multi-stage post-training paradigms for LLMs are
emerging as powerful strategies to enhance model capabilities, generalization, and alignment with
complex tasks. These approaches often involve sequential or joint fine-tuning steps, such as supervised
ifne-tuning (SFT) followed by preference learning (e.g., RLHF or DPO), or modular training where
diferent components of a system are specialized and refined in stages. Recent research highlights the
limitations of simple sequential post-training, showing that models can “forget" earlier training stages,
and proposes joint or co-training frameworks to mitigate this issue and improve overall performance.
Multi-agent and multi-component paradigms, where several LLMs or modules collaborate and are
trained together (sometimes with reinforcement learning), have demonstrated superior results in tasks
requiring reasoning, tool use, or multimodal understanding [
Human-Pedagogy Inspired LLM Enhancement: Recent research on human-pedagogy inspired
enhancements for LLMs explores methods such as structured curriculum training [
The LMT2 framework operates as a continuous life-cycle. In the context of Streaming Continual Learning (SCL), we now formalize in detail the three phases i.e., Learning, Mastery, and Teaching as distinct mechanisms to balance the stability-plasticity dilemma. 4.1. Phase 1: Learning (Knowledge Acquisition via Active Streaming) In a non-stationary environment, a model cannot simply consume all incoming data indiscriminately; doing so leads to catastrophic interference and ineficiency. The “Student" phase of our framework emulates a human learner’s ability to structure incoming information, selectively attend to novel concepts, and actively resolve ambiguities. We formalize this as a four-step pipeline: Stream Structuring, Drift-Aware Gap Assessment, Structured Injection, and Active Querying.
Raw data streams are often noisy and unstructured. A human student does not memorize raw text; they convert it into structured mental models (notes, Q&A). To replicate this, we employ a Stream Structuring Module utilizing the SciQAG framework [11]. Given an incoming document batch from the stream, instead of performing standard causal language modeling, we transform into a structured set of scientific Question-Answer pairs, = {(, )}.
= SciQAG() (1) This transformation serves two SCL purposes: 1. Noise Reduction: By extracting only salient scientific facts into Q&A format, we filter out stylistic noise and irrelevant tokens that contribute to overfitting. 2. Task Formatting: It converts unsupervised stream data into an instruction-tuning format, preparing the model for the subsequent active learning step.
A core challenge in SCL is detecting when the data distribution has shifted (concept drift) or when the model lacks specific knowledge (epistemic uncertainty). We model the “Student’s" testing phase as a proxy for active learning. Before updating weights, we assess the current model against the structured stream . We utilize the Knowledge-Aware Fine-Tuning (KaFT) protocol [12] to compute a conflict score for each sample. The model attempts to answer ∈ . If the model’s internal knowledge conflicts with the stream data (indicating either a hallucination or an outdated fact due to drift), we flag this sample for high-priority learning.
= I( () ̸= ) · + I( () ≈ ) · (2) where is the sample weight, and ≫ . This mechanism acts as a filter for plasticity since the model allocates gradient updates primarily to “unknowns" (new concepts or drifts) while suppressing updates for “knowns," thereby naturally mitigating forgetting by reducing unnecessary weight perturbations on established knowledge.
Once high-drift samples are identified, we must inject this knowledge without destabilizing existing representations. We employ a Teacher-Student Distillation approach for the update step [13, 14]. Instead of raw SFT, we utilize prompt distillation. A frozen, larger teacher model (representing an oracle or a more capable past snapshot) receives the new knowledge in its context window and generates a reasoning trace. The student model is optimized to mimic this output distribution. ℒ =
∑︁ (,)∈
· KL( ℎ(|, )||(|)) This acts as a regularizer. By distilling the distribution rather than fitting hard labels, we smooth the loss landscape, allowing the model to adapt to the stream (high plasticity for high samples) while retaining the structural reasoning capabilities of the teacher.
Passive learning from a teacher is often insuficient for resolving deep ambiguities or complex drifts. To address this, we introduce an Active Querying Module inspired by the INTERACT framework [15]. When the student model encounters high uncertainty (high entropy ) in its predictions even after initial injection, it does not passively accept the loss. Instead, it enters an interactive loop . The student generates a clarifying question targeting the ambiguity, and the Teacher model provides a specific explanation .
If ( (|)) > ,
Query: ← (, ) (3) (4) The student then integrates this response into its context and re-attempts the task. This multi-turn interaction allows the model to refine its internal representations of complex concepts before weight updates occur, efectively reducing epistemic uncertainty and ensuring that only high-confidence, verified knowledge is encoded into long-term memory. This completes the “Student" phase in which the model has structured the stream, identified drift, learned via distillation and actively resolved ambiguities.
While Phase 1 handles the initial acquisition of new stream data, SCL requires that these updates be robust to noise and compatible with prior knowledge. The “Mastery" phase formalizes this as a recursive self-improvement loop, transforming the model from a passive learner into an active practitioner that validates and consolidates new information.
In a streaming setting, single-pass training on noisy data often leads to shallow minima. To enforce
robustness, we implement an Iterative Refinement Loop inspired by the YODA framework [
+1 = (|, ) We update the model weights only on the final, verified trajectory (, ). This efectively filters out stochastic noise from the stream, ensuring that gradients are computed based on high-confidence, reasoned paths rather than initial, potentially erroneous guesses.
A primary failure mode in SCL is catastrophic forgetting, where fitting the current stream erases knowledge from −1 . To mitigate this, we employ a Multi-Teacher Distillation strategy [16]. Instead of distilling from a single oracle, the student learns from an ensemble of teachers = {, 1 , 2 }. These represent snapshots of the model at diferent timesteps or specialized expert models. The update objective minimizes the divergence from the ensemble average, acting as a form of generative replay without storing raw data.
ℒ = ∑︁ · KL( (|)||(|))
∈ This forces the model to find a parameter configuration that satisfies both the current stream (via ) and historical constraints (via ), explicitly balancing plasticity and stability. (5) (6)
When a distribution shift occurs, simply updating weights can lead to incoherent internal representations.
To ensure the model understands the drift, we utilize a Meta-Introspection Module such as ReflectEvo
[
+1 () ← (, ) This mechanism acts as an internal regularizer, forcing the model to explicitly verbalize the concept drift, which aids in rapid few-shot adaptation to the new distribution.
In SCL, it is critical to verify that an update has not degraded performance on previous tasks. We implement a Peer-Review Gate using the FAIR approach [17]. Before committing the new weights , a committee of frozen peers evaluates the model on a small anchor set of historical samples. The update is accepted only if the examination score (performance stability) remains above a threshold .
Update ←
⇐⇒ Score ( ) ≥ This step prevents polluted or destabilizing updates from corrupting the long-term memory, serving as a final quality check in the streaming pipeline.
In the final phase of the SCL loop, the model transitions from a consumer of the stream to a generator. This “Teaching" phase is critical for stability; by forcing the model to articulate and restructure its knowledge for a student, we implement a form of generative replay and modular editing that solidifies long-term retention against non-stationary drift.
To prevent catastrophic forgetting of previous stream concepts, we utilize a Learning by Teaching (LbT) paradigm [18]. Instead of simply minimizing loss on the current batch, the model acts as a “Teacher" and generates synthetic instructional data ℎ (rationales, examples) for a weaker student model . The teacher optimizes its own representations to maximize the student’s learning eficiency. ℒ = −E (,)∈ℎ [log (|, Rationale )] (9) This process forces the Teacher model to generate high-fidelity, generalized representations of the data distribution. By teaching the student, the model efectively replays its internal knowledge, reinforcing its own weights against drift without needing to store the original raw data stream. (8) (7)
Merely outputting answers is insuficient for robust generalization. To ensure the model has internalized
the causal structure of the stream data, we train it to act as a Socratic Tutor [
( ) = E∼ [︃ ∑︁ (, )
]︃ =0 (10) This optimization ensures that the model learns the underlying logic and dependencies of the domain, rather than just surface-level correlations, making it more robust to adversarial shifts in the data stream.
Finally, to handle the “Plasticity-Stability" dilemma in a perpetually non-stationary environment, we employ the WISE Framework** (Working & Side Memory Editing) [20]. We decouple the model’s memory into two components: 1. Main Memory (Θ ): Frozen or slowly updating parameters containing general reasoning capabilities (Stability).
2. Side Memory (Θ ): Rapidly updating adapter modules that store specific, time-sensitive facts from the stream (Plasticity).
A routing mechanism () determines which memory to access for a given query . = () · (Θ ; ) + (1 − ()) · (Θ ; ) (11) When the stream introduces a factual update (e.g., "The Prime Minister has changed"), we edit only the relevant shard in Θ . This allows for precise, localized updates to handle concept drift without catastrophic interference with the global model, enabling true lifelong learning.
We applied LMT2 to Llama-3.2-1B-Instruct for the topic of diferential equations. The seed documents uploaded include just a single chapter, titled “Diferential Equations” from a K-12 mathematics textbook. The evaluation dataset consisted of the corresponding questions from the Big-Math dataset [21], thereby avoiding the possibility of data contamination. The resulting LMT2 tuned model outperformed the base model by a significant margin of 33% indicating the potential of our pipeline. Due to the generic nature of LMT2, it can be generalized to domains apart from mathematics as well.
In this paper, we have introduced LMT2, a novel training paradigm that represents a fundamental departure from current approaches to developing LLMs. We have argued that the path to more robust, generalizable, and adaptive AI lies not in piecemeal, modular enhancements, but in redesigning the training process itself to be more holistic and developmental. By proposing a framework that simulates the complete human ’student-to-teacher’ life-cycle, we have provided a concrete methodology for exploring two of the most critical questions in AI research: whether deep simulation can lead to genuine replication of a persona’s capabilities, and whether a human-centric developmental journey constitutes a superior training paradigm. The framework, with its 3 distinct phases of “In the Classroom," “Mastery" and “The Teacher", is not merely a collection of techniques, but an integrated, cognitive-inspired narrative. It is our belief that by building models that learn in a way that is more analogous to our own journey of intellectual growth, we can begin to bridge the gap between the brittle intelligence of current systems and the fluid, adaptable intelligence that remains the hallmark of the human mind.
The author would like to thank Professor Sashikumaar Ganesan, from the Department of Computational and Data Science at Indian Institute of Science, Bangalore for providing valuable insightful feedback and the adequate compute resources required to execute this project.
During the preparation of this work, the author used Large Language Models (GPT-5.2, Claude Opus 4.5 and Gemini-3) as a writing assistant tool for drafting content, to generate literature review, for abstract drafting, to paraphrase and reword, to improve writing style, for grammar and spelling check as well as to generate the images used in the paper. The process was interactive. After writing the core content, the author used LLMs with specific prompts to refine the text. These prompts included requests to “check for grammatical errors,” “rephrase this sentence for clarity,” “make this paragraph more concise,” or “suggest alternative phrasing to improve flow.” The LLMs were not used to generate any scientific ideas, experimental results, data analysis or other core intellectual contributions of the paper. After using these tool(s)/service(s), the author reviewed and edited the content as needed and takes full responsibility for the publication’s content. [11] Y. Wan, A. Ajith, Y. Liu, K. Lu, C. Grazian, B. Hoex, W. Zhang, C. Kit, T. Xie, I. T. Foster, Sciqag: A framework for auto-generated scientific question answering dataset with fine-grained evaluation, arXiv preprint arXiv:2405.09939 (2024). [12] Q. Zhong, L. Ding, X. Cai, J. Liu, B. Du, D. Tao, Kaft: Knowledge-aware fine-tuning for boosting llms’ domain-specific question-answering performance, arXiv preprint arXiv:2405.15480 (2024). [13] K. Kujanpää, H. Valpola, A. Ilin, Knowledge injection via prompt distillation, 2024. URL: https: //arxiv.org/abs/2412.14964. arXiv:2412.14964. [14] G. Kim, D. Jang, E. Yang, Promptkd: Distilling student-friendly knowledge for generative language models via prompt tuning, arXiv preprint arXiv:2402.12842 (2024). [15] A. Kendapadi, K. Zaman, R. R. Menon, S. Srivastava, Interact: Enabling interactive, question-driven learning in large language models, arXiv preprint arXiv:2402.11388 (2024). [16] Y. Tian, Y. Han, X. Chen, W. Wang, N. V. Chawla, Beyond answers: Transferring reasoning capabilities to smaller llms using multi-teacher knowledge distillation, in: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025, pp. 251–260. [17] Z. Li, Y. Ji, R. Meng, D. He, Learning from committee: Reasoning distillation from a mixture of teachers with peer-review, arXiv preprint arXiv:2401.03663 (2024). [18] X. Ning, Z. Wang, S. Li, Z. Lin, P. Yao, T. Fu, M. Blaschko, G. Dai, H. Yang, Y. Wang, Can llms learn by teaching for better reasoning? a preliminary study, Advances in Neural Information Processing Systems 37 (2024) 71188–71239. [19] D. Dinucu-Jianu, J. Macina, N. Daheim, I. Hakimi, I. Gurevych, M. Sachan, From problem-solving to teaching problem-solving: Aligning llms with pedagogy using reinforcement learning, arXiv preprint arXiv:2505.15607 (2025). [20] P. Wang, Z. Li, N. Zhang, Z. Xu, Y. Yao, Y. Jiang, P. Xie, F. Huang, H. Chen, Wise: Rethinking the knowledge memory for lifelong model editing of large language models, Advances in Neural Information Processing Systems 37 (2024) 53764–53797. [21] A. Albalak, D. Phung, N. Lile, R. Rafailov, K. Gandhi, L. Castricato, A. Singh, C. Blagden, V. Xiang, D. Mahan, et al., Big-math: A large-scale, high-quality math dataset for reinforcement learning in language models, arXiv preprint arXiv:2502.17387 (2025).