Dynamic and Continual Learning in Language Models

Table of Contents

1. 🚀 Introduction

2. 💥 Catastrophic Forgetting and Mitigation

3. 🏋️ Continual Fine-Tuning vs. Static Pretraining

4. 📉 Low-Rank Adaptation (LoRA) & Adapters

5. 🔍 Retrieval-Augmented Learning

6. 🧠 Memory Modules & Adaptive Architectures

7. 🛠️ Production Implementations & Toolkits

🚀 Introduction

Large Language Models (LLMs) traditionally undergo a one-time pretraining on static data, but real-world applications demand models that can evolve with new data, tasks, and languages. The challenge is to update an LLM’s knowledge post-deployment without retraining from scratch (Reuse, Don’t Retrain: A Recipe for Continued Pretraining of Language Models),

This capability—continual learning—allows an LLM to adapt to emerging information (new facts, regulations, user preferences) and specialized domains (finance, medicine, low-resource languages) on demand (here), However, naive fine-tuning on new data often triggers catastrophic forgetting, where gains on new tasks erase prior ( Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal) knowledge. Recent research (2024–2025) has thus focused on dynamic learning techniques that strike a balance: integrating new knowledge while preserving or even enhancing the model’s existing capabilities, Below, we explore cutting-edge strategies—ranging from full model fine-tuning and lightweight adapters to retrieval-based augmentation and novel memory architectures—and how they prevent forgetting, improve efficiency, and are applied in practice.

💥 Catastrophic Forgetting and Mitigation

Updating an LLM incrementally can lead to catastrophic forgetting, where the model “unlearns” past knowledge when learning new information , To mitigate this, continual learning methods fall into several categories:

• Rehearsal (Experience Replay): Preserve a buffer of past data and mix it with new training data so the model periodically “rehearses” it. For example, a straightforward approach is to keep a small subset of original training examples and interleave them when fine-tuning on new data. This helps maintain prior knowledge but storing real user data can be impractical or violate privacy.

• Synthetic Rehearsal: Instead of storing real data, use the model itself to generate pseudo-data from previous tasks. Self-Synthesized Rehearsal (SSR) is a 2024 technique where the LLM generates synthetic examples representative of old tasks and then uses them for rehearsal. SSR was shown to match or outperform standard rehearsal while requiring no real data, SSR uses the base LLM (gray llama) to produce synthetic rehearsal data when past data is unavailable, helping the updated LLM retain prior abilities.*

• Regularization: Add a penalty to the training loss to discourage the model’s weights from straying too far from their original values. Classic examples include Elastic Weight Consolidation (EWC) and Synaptic Intelligence, which were initially applied in vision. These methods constrain weight updates on parameters important to old tasks, thereby preserving previous functionality. However, tuning such penalties for LLMs is non-trivial

• Architectural Freezing or Partitioning: Freeze portions of the network or allocate separate parameters for each task, For instance, one strategy is to freeze lower layers of the LLM (which capture general language features) and only fine-tune higher layers for new tasks. This was observed to reduce interference and “spurious forgetting” in experiments (Spurious Forgetting in Continual Learning of Language Models), Another approach is to add new modules (e.g. extra feed-forward blocks or adapters) for new knowledge, rather than overwriting the existing ones. This isolates changes to dedicated parts of the model. However, it increases model size and complexity, so there’s a trade-off

Modern research often combines these ideas. For example, the 2024 Optimal Brain Iterative Merging method merges weights from multiple fine-tuned model copies to balance new vs. old task performance (GitHub - Wang-ML-Lab/llm-continual-learning-survey: Continual Learning of Large Language Models: A Comprehensive Survey)537, In practice, ensuring an “adaptive yet stable” model entails a mix of data replay (or generation), careful training schedules, and sometimes novel modules as we discuss next.

🏋️ Continual Fine-Tuning vs. Static Pretraining

Continual fine-tuning (or continued pretraining) directly updates all or most model weights on new data distributions over time, as opposed to keeping the model static after the initial pretrain. This approach most closely mimics training from scratch on an expanded dataset, and often yields the highest raw performance on the new data. However, it is computationally intensive and prone to forgetting if done naïvely. Key developments in 2024–2025 have improved how we fine-tune LLMs continuously:

• Data Mixing and Scheduling: Rather than training on only new data, researchers found that interleaving some original data or using staged training can maintain prior knowledge. A 2024 NVIDIA study “Reuse, Don’t Retrain” showed that the choice of data distribution and learning-rate schedule is critical to avoid degradation (Reuse, Don’t Retrain: A Recipe for Continued Pretraining of Language Models), By first training on a mix of general data then gradually focusing on new domain data (with a carefully decaying learning rate), they achieved ~9% average accuracy gain on a 15B model versus naive continued pretraining In essence, start fine-tuning gently (perhaps even include some original data) and only later hone in on new data – this prevents the model from immediately overwriting its base knowledge

• Vertical vs. Horizontal Adaptation: Continual learning in LLMs can be “vertical” – specializing from a general base model to a specific domain (e.g. take a general GPT-3 and continually train it on medical texts to get a medical expert model) – or “horizontal” – updating a model over time with new knowledge while keeping its broad capabilities ( Continual Learning of Large Language Models: A Comprehensive Survey), Continual fine-tuning is used in both cases: e.g., Domain-Adaptive Pre-Training (DAPT) tunes a model further on domain-specific corpora, and periodic refreshes of models (like GPT-4 updates) incorporate the latest data. The challenge is to improve on the new domain without collapsing performance on open-domain tasks. Techniques like gradual unfreezing (first train new domain on later layers, then earlier layers) and low learning rates can help.

• Batch Adaptation vs. Online Updates: Production LLM providers (OpenAI, etc.) often do batch fine-tuning: accumulate new training data over weeks, then run a fine-tuning job to produce a refreshed model (like GPT-3.5 getting an update with more recent knowledge). Cutting-edge research is pushing toward online learning, where an LLM could update continuously on a stream of data (perhaps even user interactions) with mini-batches. Online fine-tuning is still experimental given the risk of drift, but frameworks for streaming data training are emerging (e.g. MAD-X extension for adapters, or continuum learning libraries in PyTorch). The core idea is to treat each new data batch as a tiny fine-tuning step while using the above safeguards (rehearsal, regularization) to keep the model stable.

Continual full-model fine-tuning is powerful but often a last resort due to cost. The high computational load (updating tens of billions of weights) and the need to store previous data make it expensive. Therefore, parameter-efficient alternatives have gained popularity to inject new knowledge at lower cost and with less forgetting, as we discuss next.

📉 Low-Rank Adaptation (LoRA) & Adapters

One major 2024 trend is using parameter-efficient tuning methods for continual learning. These approaches keep the original model weights mostly frozen and learn a much smaller set of new parameters for new data/tasks. This drastically reduces compute and memory requirements and often helps limit catastrophic forgetting by not drastically altering the original weights (Fine-Tuning LLMs: Overcoming Catastrophic Forgetting),

• LoRA (Low-Rank Adaptation): LoRA injects trainable low-rank matrices into the model’s weight layers. For example, each large weight matrix WW in the transformer is augmented as W+ΔWW+ΔW, where ΔW=A×BΔW=A×B and A,BA,B are small rank-rr matrices. During fine-tuning, only AA and BB are learned (the original WW s (Fine-Tuning LLMs: Overcoming Catastrophic Forgetting) tay frozen). This drastically reduces the number of tunable parameters (often by factors of >1000×) while still allowing the model to adjust its representations for the new task. In practice, LoRA can fine-tune a multi-billion parameter LLM using only a few million new parameters. It became a go-to solution in 2023 and by 2024 it’s widely used for continual updates on edge devices and for domain adaptation.

• Effect on Forgetting: Initially, researchers hoped that freezing the backbone and only shifting it via low-rank updates would inherently preserve original knowledge, After all, if the base model isn’t directly modified, one might expect it not to forget. However, recent analysis found LoRA isn’t a panacea: if the new task is very different, the LoRA modules can still effectively override the model’s behavior on old inputs. A study in 2024 observed that LoRA fine-tuning on sequential tasks did not prevent performance loss on the original tasks (Fine-Tuning LLMs: Overcoming Catastrophic Forgetting), The LoRA modules may cause subtle distribution shifts that accumulate. Thus, LoRA may be combined with rehearsal or regularization when used in a continual setting (e.g. fine-tuning with LoRA but also periodically evaluating on old tasks and adjusting training accordingly).

• Adapters and Prompt Tuning: Beyond LoRA, other light-weight tuning methods include adapters (small new feed-forward layers added between transformer blocks) and prefix/prompt tuning (learning additional token embeddings or prompts that steer the model). Adapters can be trained per task and then merged or toggled as needed. An emerging idea is to have a pool of adapters and a mechanism to route inputs to the appropriate adapter – effectively giving the model “expert modules” for different domains. One 2023–2024 approach, Continual Parameter-Efficient Tuning (ConPET), trains separate small adapter modules for each task and uses a selector network to choose which adapter to apply for a given input ( ConPET: Continual Parameter-Efficient Tuning for Large Language Models), This keeps tasks from interfering by isolating their parameters. Another 2024 technique, SPARC, learns subspace-based prompts to minimize forgetting across tasks (GitHub - Wang-ML-Lab/llm-continual-learning-survey: Continual Learning of Large Language Models: A Comprehensive Survey), Generally, these methods focus on expanding capacity slightly for new information instead of overwriting what’s already learned.

• Performance and Tooling: Parameter-efficient methods often match full fine-tuning performance on the new task, especially for smaller shifts (e.g. style adaptation, adding a new skill, They shine in production because multiple adapters or LoRA modules can be pre-loaded and swapped at inference time. For instance, an English LLM can have a French language adapter that is activated for French inputs, effectively making it bilingual without retraining the core model. Libraries like Hugging Face PEFT (Parameter-Efficient Fine-Tuning) integrate LoRA, prefix tuning, etc., making it easy for engineers to apply these techniques in PyTorch. Many companies (including Mistral AI) fine-tune models via LoRA for customers – Mistral’s documentation even provides recommended LoRA hyperparameters (e.g. learning rate ~1e-4) for their models (Fine-tuning | Mistral AI Large Language Models)l. The consensus by 2025 is that LoRA/adapters are indispensable for continual learning when compute or memory is limited.

🔍 Retrieval-Augmented Learning

Another powerful paradigm that emerged to keep LLMs up-to-date is Retrieval-Augmented Generation (RAG). Instead of (or in addition to) altering the model’s weights, RAG equips the model with access to an external knowledge repository and retrieves relevant information on the fly to answer the query (Retrieval Augmented Generation (RAG) in 2024: Future of LLMs). This approach turns the problem of “learning new information” into one of information retrieval, thereby avoiding catastrophic forgetting entirely in the model’s weights ( Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning).

• How RAG Works: A RAG system has a retriever (e.g. a vector search over documents or a database) and the LLM generator (Retrieval Augmented Generation (RAG) in 2024: Future of LLMs), When a query comes in, the retriever first fetches documents or facts relevant to the query from the knowledge source. These retrieved texts are then provided to the LLM (typically by prepending them to the prompt or encoding them in a separate input). The LLM generates its answer conditioned not only on its internal knowledge but also on this fresh external context, Crucially, the knowledge source can be updated continuously (e.g. add new documents, update entries in a wiki) without touching the LLM’s weights.

• Dynamic Knowledge with No Weight Update: Because the model isn’t being re-trained for new information, it doesn’t forget old knowledge – it still has its original trained knowledge intact, and simply supplements it with retrieved facts. For example, if an LLM was trained up to 2022 data, and we want it to handle 2024 events, we can equip it with a search tool or a up-to-date vector index of news articles. When asked about a 2024 event, the model pulls in the relevant article and can discuss it. This strategy was adopted in production by systems like Bing Chat and Bard, where the LLM (GPT-4 or PaLM2) queries a web search or internal index to get current information rather than relying purely on its stale training data.

• Preventing Forgetting vs. Efficiency Trade-off: Retrieval augmentation cleanly sidesteps forgetting by keeping new knowledge external ( Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning). The model’s parameters don’t need to change at all to incorporate new facts. However, the trade-off is that the model’s inference speed and complexity are affected – each query now requires a retrieval step, and the model must process extra context (which can be large). If the knowledge base is huge, retrieving and reading a lot of text can slow down responses. Research in late 2024 addressed this with smarter integration: e.g. the RECIPE method (EMNLP 2024) trains a joint retriever and a continuous prompt encoder to inject retrieved facts into the model more efficiently. RECIPE converts new knowledge into learned prompt embeddings that are concatenated to the input, rather than raw text, and uses a learned threshold (a “Knowledge Sentinel”) to decide when retrieval is needed . This kind of approach blurs the line between retrieval and model update, but fundamentally the model’s original weights remain unchanged – the new info is only in the prompts. The authors demonstrate fast inference and reliable knowledge editing without forgetting.

• Applications: Many real-world systems favor retrieval for dynamic knowledge: customer support bots retrieve policy documents, coding assistants retrieve API docs or past code, etc. In 2025, we also see hybrid setups: a base model is periodically fine-tuned on very common new knowledge, but rare or time-sensitive facts are left to retrieval. This minimizes weight changes while still giving good coverage. Notably, OpenAI’s plugin ecosystem and tools like LangChain have made it straightforward to build RAG pipelines with OpenAI or open-source models. The bottom line is RAG provides an “update” mechanism that is orthogonal to weight training – by updating the database, you update what the model can output, with zero risk of forgetting.

🧠 Memory Modules & Adaptive Architectures

Beyond fine-tuning and retrieval, researchers have introduced architectural innovations to endow LLMs with a form of long-term memory or dynamic modularity. These approaches modify the model’s structure so it can store new information over time, often with dedicated components that can be updated independently of the rest of the network. Two notable themes in 2024–2025 are internal memory modules and mixture-of-experts routing:

• Latent Memory Pools: Instead of relying on the fixed context window of a transformer, latent memory architectures give the model read-write access to a large memory within its layers. MemoryLLM (ICML 2024) introduced a transformer augmented with a fixed-size memory pool of 1 billion parameters interleaved into the layers (M+: Extending MemoryLLM with Scalable Long-Term Memory), During training, this memory pool stores representations of new knowledge and is periodically updated using a specialized update process (some memory slots are overwritten with new info, some old info is dropped out, Crucially, MemoryLLM’s memory can be continually updated with new textual knowledge (via additional training on memory slots) without altering the base transformer weights . The result is a model that “can self-update with text knowledge and memorize the knowledge injected earlier” while maintaining long-term performance. Impressively, it showed no performance degradation after nearly a million memory update operations, indicating sl . Building on this, M+ (2024) extended MemoryLLM with a scalable long-term memory: it offloads older memory slots to a CPU-based store and uses a co-trained retriever to fetch relevant long-term memories, This allowed extending the effective context from 20k tokens to 160k+ tokens of retained information (Daily Papers - Hugging Face). These memory-augmented models blur the line between model weights and external storage – they have a component that behaves like an external knowledge base (hence reducing forgetting of past info) but is tightly integrated into the model’s forward pass.

• Mixture-of-Experts (MoE) and Routing: MoE architectures increase model capacity by having multiple expert sub-networks and a learned router that activates different experts for different inputs (Noteworthy LLM Research Papers of 2024), In a continual learning context, MoEs are attractive because new experts can be added for new tasks or domains without disturbing existing ones. In early 2024, Mistral AI unveiled Mixtral 8×7B, an 8-expert MoE version of Llama-2, which replaces each transformer feed-forward layer with 8 parallel feed-forward networks, A gating network (router) directs each token through one of the 8 experts, so at runtime only a subset of the model is used for an input token. By adding experts, the model’s knowledge capacity grows, and importantly one can train a new expert on a new dataset while leaving the others untouched, mitigating interference. For example, if an LLM needs to learn programming knowledge, one could train a new “coding expert” layer, and the router will learn to dispatch code-related inputs to that expert. The base knowledge (in other experts) remains intact. This approach was noted to preserve prior performance while efficiently allocating new capacity, MoEs are supported by frameworks like DeepSpeed-MoE in PyTorch which handle the routing and distributed training. By 2025, MoEs aren’t yet mainstream in all deployed models (many production models still use dense models, but they are actively studied as a way to achieve continual scaling – adding experts over time to grow the model’s skills without retraining everything This allows adding new “experts” for new tasks, reducing interference with existing knowledge.

• Progressive Layer Expansion: Another architectural idea is growing the model as it learns new tasks. Instead of a fixed number of layers, a model can gain new layers or neurons for new information. For instance, LLaMA-Adapter v2 (2023) and a 2024 approach dubbed LLaMA-DC allowed appending small adapter layers for new domains, and LLaMA-Pro explored block-wise expansion (adding transformer blocks) as training progresses (GitHub - Wang-ML-Lab/llm-continual-learning-survey: Continual Learning of Large Language Models: A Comprehensive Survey), The model effectively “expands” rather than overwrites. While unlimited growth is not feasible, moderate expansion can be a way to allocate new capacity when the existing network is at capacity with old knowledge. Such models often include a compression or pruning step later to control size (ensuring the most useful new parameters are kept).

In summary, architectural innovations provide LLMs with a form of built-in continual learning: memories to store new info, or modular experts to handle new skills. These come with implementation complexity, but 2024 results (MemoryLLM, M+, etc.) are promising, showing that models can sustain long-term knowledge injection with minimal forgetting (MEMORYLLM: Towards Self-Updatable Large Language Models),

🛠️ Production Implementations & Toolkits

Leading AI companies have begun incorporating the above techniques into their production pipelines to keep models current:

• OpenAI: While details of OpenAI’s internal methods are scarce, they offer fine-tuning APIs for models like GPT-3.5 and GPT-4, allowing users to continually train custom versions on new data. OpenAI likely uses a combination of full fine-tuning with careful regularization and segmented training runs. They have also effectively used retrieval: ChatGPT with the browsing plugin or system message instructions can fetch latest info instead of relying on an outdated knowledge cutoff. This suggests OpenAI values RAG for dynamic knowledge (so the base model doesn’t need constant retraining for factual updates). OpenAI’s deployment of tools (e.g. code interpreter, web browser) can be seen as a way to extend the model’s capabilities continually without modifying the core model weights – the tools encapsulate new “skills” or data access.

• Meta (Facebook): Meta’s LLaMA series has embraced open fine-tuning. LLaMA and LLaMA-2 models were released with weights and have since been fine-tuned by the community on countless datasets (Alpaca, instruct tuning, etc.). Meta researchers have explored adapters (they introduced the original Adapter approach in 2022) and LoRA for efficient fine-tuning on consumer hardware. In late 2024, Meta released models like Llama-2-Chat which were effectively continually fine-tuned versions of Llama-2 on conversational data. For internal use, Meta likely employs continual pretraining on fresh social media data to improve their models’ knowledge. They also open-sourced tools like AVA and FTC that evaluate forgetting and domain shift (GitHub - Wang-ML-Lab/llm-continual-learning-survey: Continual Learning of Large Language Models: A Comprehensive Survey). Meta has shown interest in MoE models (e.g. the Mixtral collaboration with Mistral AI) to potentially deploy larger-but-sparse models. For engineers, the PyTorch-based implementations of LLaMA make it straightforward to apply LoRA, and Meta’s research code often includes recipes for replay and regularization when fine-tuning.

• Google: Google’s PaLM 2 and the upcoming Gemini are kept up-to-date using a mix of Domain-Adaptive Pretraining and retrieval. Google’s Bard, for example, integrates Google Search results into its answers (a RAG approach). For domain adaptation, Google has published techniques on continual instruction tuning and reward tuning – e.g. daily fine-tuning of their models on user feedback data (a form of continual RLHF). In 2024, Google researchers published a Lifelong Learning analysis where they train language agents that accumulate knowledge via dialogue and store it in memory (Guys, did Google just crack the Alberta Plan? Continual learning ...)1. In terms of tooling, Google’s TensorFlow and JAX ecosystems (e.g. T5X for T5 models) support continual training – one can resume pretraining on new data using the same infrastructure that did the original training. They have also developed efficient data pipelines to mix streams of new and old data (important for preventing forgetting).

• Mistral AI: As a newer startup, Mistral has been aggressive in implementing fine-tuning as a service. Their documentation highlights one-click fine-tuning with LoRA on their platform (Fine-tuning | Mistral AI Large Language Models)1. They also have shown that even language adaptation (e.g. teaching Mistral 7B a new language) is feasible with a few hours of fine-tuning. Mistral contributed to MoE research (Mixtral model) to explore scaling up model capacity without starting from scratch. For developers, Mistral’s open-source codebase provides a lightweight fine-tuning library that implements memory-efficient optimizations (gradient checkpointing, QLoRA with 4-bit quantization (mistralai/mistral-finetune - GitHub)n). This allows continual learning on consumer GPUs.

• Tools and Libraries: Across the industry, there is a rich toolbox for continual learning: Hugging Face Transformers (supports training from checkpoints, adapters, LoRA), PEFT library (for easy insertion of LoRA/prompt tuning in PyTorch models), TRL (Transformer Reinforcement Learning, for continual RLHF on rewards), and academic libraries like Avalanche (by ContinualAI, which provides workflows for incremental training and evaluation of forgetting). JAX/Flax users leverage Optax and FlaxLLM which support streaming data updates. On the retrieval side, vector database tools (e.g. FAISS, Weaviate) and libraries like LangChain have made it simple to build retrieval-augmented LLM apps. In 2025, we even see hybrid approaches packaged together: for example, Hugging Face’s AutoTrain will fine-tune a model on new data and also set up a retrieval index for it if provided a knowledge corpus, covering both weight-based and retrieval-based updating.

In conclusion, dynamic learning for LLMs has rapidly advanced in 2024–2025. Techniques like careful continual fine-tuning with rehearsal, low-rank adaptation, retrieval augmentation, and memory-augmented architectures each contribute solutions to the puzzle of keeping an AI model current, knowledgeable, and resilient against forgetting. Engineers now have a spectrum of approaches to choose from – from simply plugging in a retrieval system for instantaneous updates, to lightweight fine-tuning for domain adaptation, up to sophisticated architectures that can grow and remember over a long horizon. The state-of-the-art is moving toward LLMs that can learn continuously like humans, updating themselves regularly while preserving the wisdom they’ve already acquired. With robust tool support in PyTorch, JAX, and TensorFlow, these research breakthroughs are quickly translating into practice, enabling AI systems that stay relevant in our ever-changing world.