Research task (AI & ML):
Write a series of technical blog posts on some of the advancements in Large Language Model (LLM) training and fine-tuning in 2023-2025. Each post should at least contain:
- The intuition and motivation behind the technique
- Detail the methodology
- Provide mathematical derivations where applicable
- Discuss practical use cases, limitations, and open challenges

The writing should be accessible to readers with a graduate-level ML background.

Evaluation rubric:
criterion: All acronyms (e.g., RLHF: Reinforcement Learning with Human Feedback, PEFT: Parameter-Efficient Fine-Tuning, GRPO: Group Relative Policy Optimization, MoE: Mixture of experts, LoRA: Low-Rank Adapation) are defined on first use.
weight: 5
axis: Implicit Criteria

criterion: The introduction to each blog post highlights a key feature of technique discussed (e.g., LoRA reduces the number of trainable parameters, RLHF trains a reward model based on human feedback, MoE routes inputs to smaller expert networks, GRPO uses the mean reward of several LLM responses as a baseline to score a response).
weight: 3
axis: Communication Quality

criterion: The response includes a diagram along the lines of a  "Series roadmap" (e.g., a flowchart, mind map, visual chronology, figure depicting a transformer training pipeline annotated with techniques) that visually outlines the conceptual connections and dependencies between all discussed techniques.
weight: 3
axis: Communication Quality

criterion: The introduction of each post contains a concise recap of a prior post connected to the technique being currently discussed.
weight: 4
axis: Implicit Criteria

criterion: The response includes an introductory, dedicated section along the lines of a "Series Overview."
weight: 5
axis: Implicit Criteria

criterion: The concluding remarks of each blog post before the last includes a teaser for the next topic.
weight: 4
axis: Implicit Criteria

criterion: The response provides a mathematical description of each technique (e.g., the LoRA  treats the gradient as a low-rank decomposition ΔW=AB, the reward-function for GRPO, the loss function for the MoE router, the actor-critic policy for LLM preference training).  
weight: 5
axis: Explicit Criteria

criterion: All included mathematical derivations have no visible rendering errors (e.g., unparsed LaTeX code, equations which run off the line, subscript/superscript mistakes, words within equations not placed in \mathrm or \textrm mode).
weight: 4
axis: Miscellaneous

criterion: The response outlines the general intuition and motivation for each technique.
weight: 5
axis: Explicit Criteria

criterion: For all techniques, the response discusses practical use cases about how those techniques have been applied.
weight: 5
axis: Explicit Criteria

criterion: The response discusses techniques primarily related to LLM inference or application that are not training/fine-tuning methods (e.g., Retrieval-Augmented Generation, Chain-of-Thought prompting, Agents, Multimodal Models).
weight: -5
axis: Explicit Criteria

criterion: The response outlines the methodology of each technique including any algorithms, pseudocode, or procedures as necessary.
weight: 5
axis: Explicit Criteria

criterion: Each discussed technique that is mentioned includes an in-depth evaluation of its limitations.
weight: 5
axis: Explicit Criteria

criterion: The response is structured as a series of distinct blog posts, each with its own title, introduction, body, and conclusion.
weight: 5
axis: Explicit Criteria

criterion: The response discusses at least three distinct recent advancements within the last 12-24 months (e.g., QLoRA, DoRA, DPO, MoE, RAFT).
weight: 5
axis: Explicit Criteria

criterion: The response should mention the general publication date of the associated arxiv paper, blog, journal article, technical, etc., of each technique to demonstrate recency.
weight: 2
axis: Implicit Criteria

criterion: The response includes at least one pseudocode example for a discussed technique.
weight: 2
axis: Communication Quality

criterion: The response discusses at least one technique that is an evolution of or alternative to Direct Preference Optimization (DPO) (e.g., Identity Preference Optimization (IPO), Kahneman-Tversky Optimization (KTO), Reinforcement Learning from Human Feedback (RLHF), Chain-of-Hindsight (CoG)). 
weight: 4
axis: Implicit Criteria

criterion: There is a discussion about open challenges for each advancement discussed.
weight: 5
axis: Explicit Criteria

criterion: For each technique, justify why it merits coverage (e.g., LoRA significantly reduces training costs, MoEs enable multiple experts with sparse activations, RLHF enables human-aligned response styles, RAFT trains models to ignore irrelevant context drawn via the popular RAG strategy).
weight: 3
axis: Implicit Criteria

criterion: Each blog post includes at least one diagram or illustrative figure with a descriptive caption.
weight: 5
axis: Communication Quality

criterion: The response includes discussion or an ablation study to emphasize the impact of each technique on the training pipeline versus a common alternative (e.g., comparing the full Direct Preference Optimization (DPO) pipeline against a variant that omits the preference loss term to measure its effect on alignment win-rate and calibration error; comparing the increased memory usage of LoRA which uses full-precision against QLoRA which uses quantized updates; examining the increased number of activated weight parameters for a vanilla LLM versus a mixture-of-experts (MoE) architecture which activates only a small percentage of parameters associated to an expert; comparing the increased runtime complexity of proximal policy optimization (PPO) because it uses a separate valuation critic vs group-relative policy optimization (GRPO) which simply judges a response against a group of other generated LLM responses.).
weight: 4
axis: Synthesis of Information

criterion: The response contextualizes each technique by explicitly comparing or contrasting it with a more established, foundational technique the target audience is expected to know (e.g., directly framing DPO in relation to PPO-based RLHF, LoRA versus SGD, MoEs in relation to general ensemble learning, GRPO eliminating the critic, RAFT versus non-RAG fine-tuning).
weight: 4
axis: Synthesis of Information

criterion: The response provides foundational machine learning concepts that would be considered too basic of knowledge for the target graduate-level ML audience (e.g., it explains or defines the concept of a neural network, gradient descent, hyperparameters, loss function).
weight: -4
axis: Explicit Criteria

criterion: The response discusses advancements which are notable among the ML community (e.g., high citation count; published at NeurIPS, ICML, ICLR, ACL, etc.; technical reports filed by OpenAI, Anthropic, NVIDIA, Qwen, DeepSeek, Hugging Face etc.; implementations supported by popular libraries such as PyTorch, TensorFlow, LangChain, etc.). 
weight: 4
axis: Implicit Criteria

criterion: The response indicates that these fine-tuning advancements are needed due to the sheer size of LLM architectures and the large volume of training data required to train them. 
weight: 4
axis: Synthesis of Information

criterion: The response links each fine-tuning technique to the nature of training over language in an autoregressive manner (e.g., attention considers all previous context thus encouraging fine-tuning techniques such as RAFT to ignore irrelevant context appended via RAG, differing models can emit very different output to samples thus encouraging output averaging akin to GRPO, next-token-prediction for human-aligned preferences is difficult without human feedback thus requiring RLHF, gradient updates are low-rank for pre-trained models thus encouraging LoRA).
weight: 4
axis: Synthesis of Information

criterion: The collection of posts would be digestible for a typical blog audience and does not approach the length of an arXiv survey.
weight: 4
axis: Instruction Following