> ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. --- title: "Key Papers & Reading List" url: https://daily.dev/agentic-ai-hub/key-papers-reading-list/ description: "A chronological spine of the field. The ones tagged Start here or Landmark are the priority reads; the rest fill in context." lastUpdated: "2026-07-22" --- A chronological spine of the field. The ones tagged **Start here** or **Landmark** are the priority reads; the rest fill in context. Links go to arXiv (or the canonical source). "Why it matters" in one line each. ## Foundations (2013–2020) - **Word2Vec / "Efficient Estimation of Word Representations in Vector Space"** (2013): [arxiv.org/abs/1301.3781](https://arxiv.org/abs/1301.3781). Learned word embeddings; the vector-space intuition everything downstream inherits. - **Attention Is All You Need** (2017): [arxiv.org/abs/1706.03762](https://arxiv.org/abs/1706.03762). Introduced the Transformer. Every modern LLM descends from it. **Start here.** - **Deep RL from Human Preferences** (Christiano et al., 2017): [arxiv.org/abs/1706.03741](https://arxiv.org/abs/1706.03741). The origin of RLHF: learning a reward from human comparisons. The recipe InstructGPT later scaled to language. **Landmark.** - **BERT** (2018): [arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805). Bidirectional pretraining that made "fine-tune a big pretrained model" the default paradigm. - **GPT-2 / "Language Models are Unsupervised Multitask Learners"** (2019): [cdn.openai.com/better-language-models](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf). Scaling generative pretraining. The "too dangerous to release" moment. - **Scaling Laws for Neural Language Models** (2020): [arxiv.org/abs/2001.08361](https://arxiv.org/abs/2001.08361). Showed loss falls predictably with compute, data, and parameters; the economic logic of the whole race. - **GPT-3 / "Language Models are Few-Shot Learners"** (2020): [arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165). In-context learning at scale. The start of prompting as a discipline. **Landmark.** ## The alignment & reasoning era (2021–2023) - **LoRA** (2021): [arxiv.org/abs/2106.09685](https://arxiv.org/abs/2106.09685). Parameter-efficient fine-tuning; why you can adapt huge models on one GPU. - **Chain-of-Thought Prompting** (2022): [arxiv.org/abs/2201.11903](https://arxiv.org/abs/2201.11903). "Let's think step by step" unlocked reasoning. The seed of today's thinking models. - **InstructGPT / "Training LMs to follow instructions with human feedback"** (2022): [arxiv.org/abs/2203.02155](https://arxiv.org/abs/2203.02155). RLHF; the recipe that turned GPT-3 into something usable and became ChatGPT. **Landmark.** - **Chinchilla / "Training Compute-Optimal LLMs"** (2022): [arxiv.org/abs/2203.15556](https://arxiv.org/abs/2203.15556). Rewrote the scaling rules: most models were undertrained on data. Reshaped every training budget after it. - **ReAct** (2022): [arxiv.org/abs/2210.03629](https://arxiv.org/abs/2210.03629). Interleaves reasoning and tool use. The conceptual blueprint for agents. - **Constitutional AI** (2022): [arxiv.org/abs/2212.08073](https://arxiv.org/abs/2212.08073). RLAIF: replace human harm labels with a model critiquing against a written constitution. The basis of Anthropic's alignment approach. - **LLaMA** (2023): [arxiv.org/abs/2302.13971](https://arxiv.org/abs/2302.13971). Efficient open weights that ignited the open-source LLM ecosystem. **Landmark.** - **GPT-4 Technical Report** (2023): [arxiv.org/abs/2303.08774](https://arxiv.org/abs/2303.08774). The capability jump that mainstreamed AI; notable also for what it does not disclose. - **Toolformer** (2023): [arxiv.org/abs/2302.04761](https://arxiv.org/abs/2302.04761). Models teaching themselves to call APIs. Foundational for tool use. - **DPO / Direct Preference Optimization** (2023): [arxiv.org/abs/2305.18290](https://arxiv.org/abs/2305.18290). Preference tuning without the RL; simpler and now used everywhere. - **QLoRA** (2023): [arxiv.org/abs/2305.14314](https://arxiv.org/abs/2305.14314). 4-bit fine-tuning of 65B models on a single GPU; democratized adaptation. ## Efficiency, agents & multimodality (2021–2025) - **CLIP / "Learning Transferable Visual Models From Natural Language Supervision"** (2021): [arxiv.org/abs/2103.00020](https://arxiv.org/abs/2103.00020). Contrastive image-text pretraining; the backbone of modern multimodal models. **Landmark.** - **FlashAttention** (2022): [arxiv.org/abs/2205.14135](https://arxiv.org/abs/2205.14135). IO-aware exact attention; the kernel that made long context practical. - **Mistral 7B** (2023): [arxiv.org/abs/2310.06825](https://arxiv.org/abs/2310.06825). Small-model efficiency (GQA, sliding-window attention) that performed well above its size. - **Llama 2** (2023): [arxiv.org/abs/2307.09288](https://arxiv.org/abs/2307.09288). Open weights plus an RLHF-tuned chat model; the release that seeded the local-model ecosystem (the list has LLaMA 1 above). - **Mixtral / Mixture-of-Experts** (2024): [arxiv.org/abs/2401.04088](https://arxiv.org/abs/2401.04088). Sparse MoE at open scale; the architecture behind much frontier-model efficiency. - **Mamba / State Space Models** (2023): [arxiv.org/abs/2312.00752](https://arxiv.org/abs/2312.00752). A sub-quadratic challenger to attention; a leading candidate for what comes after Transformers. - **DeepSeek-V3** (2024): [arxiv.org/abs/2412.19437](https://arxiv.org/abs/2412.19437). The 671B open-weight MoE base that R1 is built on; efficient training at frontier scale. - **DeepSeek-R1** (2025): [arxiv.org/abs/2501.12948](https://arxiv.org/abs/2501.12948). Showed strong reasoning can emerge from RL alone, open-weight; a watershed for open reasoning models. - **RAG / "Retrieval-Augmented Generation"** (2020, read it now): [arxiv.org/abs/2005.11401](https://arxiv.org/abs/2005.11401). Grounding generation in retrieved documents. The backbone of most production LLM apps. > > **How to read the frontier:** Papers now ship faster than you can read them. Follow [People to Follow (X/Twitter)](/agentic-ai-hub/people-to-follow/) researchers for the "read this one" signal, use [alphaXiv](https://www.alphaxiv.org/) or Hugging Face's [Daily Papers](https://huggingface.co/papers) to triage, and skim abstracts + figures before committing to a full read. 🔄