---
title: AI
description: AI notes, small builds, and writeups by Mrinaal Arora
updated: 2026-10-15
canonical: https://aroramrinaal.com/ai
source: https://aroramrinaal.com/ai.md
alternate: https://aroramrinaal.com/ai/markdown
---

# AI

A home for my notes, small builds, and writeups as I study LLMs and deep learning from scratch.

- Page: https://aroramrinaal.com/ai
- Markdown index: https://aroramrinaal.com/ai/markdown
- Markdown index (.md): https://aroramrinaal.com/ai.md
- AI projects: https://aroramrinaal.com/ai/projects
- Tag index: https://aroramrinaal.com/ai/tags/markdown
- Tag index (.md): https://aroramrinaal.com/ai/tags.md
- Total posts: 20
- Tags: Agents, AI Safety, Alignment, Autoresearch, Continual Learning, ICML, Image Generation, Inference, Interpretability, Multi-Agent Systems, Multilingual, Pre-Training, RL, SFT, Sparse Autoencoders

## Tags

- Agents (1): https://aroramrinaal.com/ai/tags/agents/markdown
- AI Safety (3): https://aroramrinaal.com/ai/tags/ai-safety/markdown
- Alignment (3): https://aroramrinaal.com/ai/tags/alignment/markdown
- Autoresearch (3): https://aroramrinaal.com/ai/tags/autoresearch/markdown
- Continual Learning (1): https://aroramrinaal.com/ai/tags/continual-learning/markdown
- ICML (1): https://aroramrinaal.com/ai/tags/icml/markdown
- Image Generation (1): https://aroramrinaal.com/ai/tags/image-generation/markdown
- Inference (3): https://aroramrinaal.com/ai/tags/inference/markdown
- Interpretability (2): https://aroramrinaal.com/ai/tags/interpretability/markdown
- Multi-Agent Systems (1): https://aroramrinaal.com/ai/tags/multi-agent-systems/markdown
- Multilingual (1): https://aroramrinaal.com/ai/tags/multilingual/markdown
- Pre-Training (2): https://aroramrinaal.com/ai/tags/pre-training/markdown
- RL (4): https://aroramrinaal.com/ai/tags/rl/markdown
- SFT (3): https://aroramrinaal.com/ai/tags/sft/markdown
- Sparse Autoencoders (1): https://aroramrinaal.com/ai/tags/sparse-autoencoders/markdown

## Posts

- [Codex Claude Crew](https://aroramrinaal.com/ai/codex-claude-crew/markdown) — 2026-10-13; tags: Agents. A Codex skill that hands bounded tasks to a local Claude Code session and gets the result back for Codex to check. Codex writes the brief, Claude reviews or implements the change in its own git worktree, and Codex verifies the work. [HTML](https://aroramrinaal.com/ai/codex-claude-crew)
- [Rumik TTS Fast Inference](https://aroramrinaal.com/ai/rumik-tts-fast-inference/markdown) — 2026-09-24; tags: Inference. 60+ experiments to make Rumik OSS 1 faster: 10.74× the stock inference speed on one H100, with automated speech-quality screening. [HTML](https://aroramrinaal.com/ai/rumik-tts-fast-inference)
- [LittleLearner Experiments: Teaching Math Beyond K–5](https://aroramrinaal.com/ai/little-learner/markdown) — 2026-09-02; tags: Continual Learning, SFT. I trained 16 LoRA adapters on K–5-constrained LittleLearner models and found that useful mathematical skill transfer changed with model scale. [HTML](https://aroramrinaal.com/ai/little-learner)
- [Can a Warmth-Trained Model Learn When Not to Agree?](https://aroramrinaal.com/ai/persona-alignment-experiment/markdown) — 2026-08-20; tags: AI Safety, Alignment. I compared reasons, a matched-length control, and character training after warmth SFT; none increased judged warmth, while M4 most strongly resisted explicit persona attacks. [HTML](https://aroramrinaal.com/ai/persona-alignment-experiment)
- [When Emergent Misalignment Crosses Languages](https://aroramrinaal.com/ai/cross-lingual-emergent-misalignment/markdown) — 2026-08-17; tags: AI Safety, Alignment, Multilingual. English risky-financial fine-tuning transferred coherent emergent misalignment into Hindi, Marathi, and Urdu, while matched prudent controls stayed near zero. [HTML](https://aroramrinaal.com/ai/cross-lingual-emergent-misalignment)
- [Making an Image Editor with LongCat and a Small LoRA](https://aroramrinaal.com/ai/image-editing-model/markdown) — 2026-08-12; tags: SFT, Image Generation. My first image-generation research experiment: teaching LongCat-Image-Dev source-conditioned editing, finding global regeneration, and improving preservation by filtering the data. [HTML](https://aroramrinaal.com/ai/image-editing-model)
- [Narrow Fine-Tuning, Broad Misalignment in a 3B Model](https://aroramrinaal.com/ai/emergent-misalignment/markdown) — 2026-08-10; tags: AI Safety, Alignment. I fine-tuned a 3.35B model on narrowly risky financial advice and found a reproducible shift toward coherent misalignment on unrelated prompts. [HTML](https://aroramrinaal.com/ai/emergent-misalignment)
- [Before It Codes: Catching Qwen3.5-4B Planning With J-Lens](https://aroramrinaal.com/ai/j-lens/markdown) — 2026-07-30; tags: Interpretability. I used Anthropic's new Jacobian lens to test whether Qwen3.5-4B represents an algorithm before it writes code, then built a live visualizer around the result. [HTML](https://aroramrinaal.com/ai/j-lens)
- [Hybrid Token-Efficient Routing Agent](https://aroramrinaal.com/ai/hybrid-token-efficient-routing-agent/markdown) — 2026-07-22; tags: Autoresearch, Inference. How I built TOKENMAN for AMD Developer Hackathon ACT II: a deterministic, local-model, and Fireworks routing agent that climbed from 6,101 counted tokens to zero. [HTML](https://aroramrinaal.com/ai/hybrid-token-efficient-routing-agent)
- [World Fan Arena](https://aroramrinaal.com/ai/world-fan-arena/markdown) — 2026-07-16; tags: Multi-Agent Systems. A public football fan simulation where AI fan agents from different countries react to matches, rivalries, upsets, predictions and tournament drama inside chat rooms. [HTML](https://aroramrinaal.com/ai/world-fan-arena)
- [\[ICML ’26 Effort\] Efficient Qwen: Making Qwen3.5-4B Faster on a Single A10G](https://aroramrinaal.com/ai/icml-adaptfm-efficient-qwen/markdown) — 2026-06-30; tags: ICML, Autoresearch, Inference. My ICML 2026 AdaptFM effort to optimize Qwen3.5-4B for low-latency inference on a single NVIDIA A10G through 100+ runtime, compiler, and quantization experiments. [HTML](https://aroramrinaal.com/ai/icml-adaptfm-efficient-qwen)
- [Looking Inside Qwen3-4B With Sparse Autoencoders](https://aroramrinaal.com/ai/looking-inside-qwen3-4b-with-sparse-autoencoders/markdown) — 2026-06-12; tags: Interpretability, Sparse Autoencoders. I trained a sparse autoencoder on Qwen3-4B-Base, labeled its learned features, and tested one by steering the model toward cooking instructions. [HTML](https://aroramrinaal.com/ai/looking-inside-qwen3-4b-with-sparse-autoencoders)
- [Pre-Training My First Base Language Model From Scratch](https://aroramrinaal.com/ai/first-pre-train-llm/markdown) — 2026-05-13; tags: Pre-Training. Took a model from random initialization through 2B tokens of FineWeb-Edu on a single H100 and watched it learn next-token prediction from nothing. [HTML](https://aroramrinaal.com/ai/first-pre-train-llm)
- [Parameter Golf: My OpenAI Model Craft Experiment](https://aroramrinaal.com/ai/parameter-golf-openai-model-craft/markdown) — 2026-05-04; tags: Pre-Training, Autoresearch. I didn't come close to the Parameter Golf leaderboard, but I still had a lot of fun running scattered H100 experiments on Modal, hunting tiny BPB improvements while watching ideas collapse against artifact size limits, and figuring out the hard way why squeezing a capable model into 16 MB is trickier than it sounds. [HTML](https://aroramrinaal.com/ai/parameter-golf-openai-model-craft)
- [CrisisOps: My (Final Round) OpenEnv Hackathon Project](https://aroramrinaal.com/ai/crisisops-openenv-hackathon-finale/markdown) — 2026-04-29; tags: RL. How I built CrisisOps for the OpenEnv hackathon finale, then trained a small model with GRPO using TRL, Unsloth, and Modal. [HTML](https://aroramrinaal.com/ai/crisisops-openenv-hackathon-finale)
- [My First Successful RL Training Run With OpenEnv And Wordle](https://aroramrinaal.com/ai/rl-training-openenv-environment/markdown) — 2026-04-02; tags: RL. A full fine-tune of Qwen3-1.7B on Wordle using OpenEnv, with a reward curve that actually went up and shaped rewards that taught real gameplay behavior. [HTML](https://aroramrinaal.com/ai/rl-training-openenv-environment)
- [My Second RL Experiment On Prime Intellect Lab](https://aroramrinaal.com/ai/second-rl-experiment-on-prime-intellect-lab/markdown) — 2026-03-25; tags: RL. My second RL experiment while studying GRPO: moving to Prime Intellect Lab, testing smaller and larger setups, and finally getting a reward curve that went up. [HTML](https://aroramrinaal.com/ai/second-rl-experiment-on-prime-intellect-lab)
- [First RL Experimental Project: RLVR Using GRPO With TRL On Modal](https://aroramrinaal.com/ai/first-rl-experimental-project/markdown) — 2026-03-23; tags: RL. My first ever RL experiment: RLVR on GSM8K using Hugging Face TRL, Qwen2.5 1.5B Instruct, and an NVIDIA H100 on Modal, with notes on where the reward function broke and what I want to fix in v2. [HTML](https://aroramrinaal.com/ai/first-rl-experimental-project)
- [My Current Approach in Learning and Experimenting with LLMs and Deep Learning](https://aroramrinaal.com/ai/my-current-approach-in-learning-and-experimenting-with-llms-and-deep-learning/markdown) — 2026-03-18; tags: None. How I study LLMs by going deep on specific topics instead of starting from math. [HTML](https://aroramrinaal.com/ai/my-current-approach-in-learning-and-experimenting-with-llms-and-deep-learning)
- [Teaching a 3B Base Model to Emit Reasoning Traces](https://aroramrinaal.com/ai/nanbeige-cold-start-sft/markdown) — 2026-03-11; tags: SFT. A first small-scale AI research experiment: cold-start SFT on Nanbeige4-3B-Base using 2,160 distilled reasoning triplets, trained with LoRA on an H100, with notes on what worked and what I want to try next. [HTML](https://aroramrinaal.com/ai/nanbeige-cold-start-sft)