{"slug": "alignment-research-engineer-accelerator-this-is-where-the-arena-course-conten", "title": "Alignment Research Engineer Accelerator This is where the ARENA course conten", "summary": "The Alignment Research Engineer Accelerator (ARENA) hosts its full course content at arena.education, covering deep learning fundamentals, transformer interpretability, reinforcement learning, evaluations, and alignment science. The curriculum spans chapters from PyTorch prerequisites and CNNs through sparse autoencoders, activation oracles, and circuit analysis, with details on upcoming cohorts and applications available at arena.education.", "body_md": "Alignment Research Engineer Accelerator\n\n            This is where the ARENA course content is hosted. For more information about the ARENA program,\n            including upcoming cohorts and how to apply, visit [arena.education](https://arena.education).\n        \n\n## Fundamentals\n\nBuild your foundation in deep learning, from prerequisites through CNNs, optimization, backpropagation, and generative models.\n\n## Interpretability\n\nDive deep into language model interpretability, from linear probes and SAEs to circuit analysis and toy models.\n\n## RL\n\nTake a whirlwind tour through RL, starting from tabular learning and Atari, and ending with some of the cutting-edge techniques used in current LLM post-training.\n\n## Evals\n\nLearn to build and run evaluations for large language models, including dataset generation and LLM agents.\n\n## Alignment Science\n\nCase studies in misalignment, covering a range of topics and techniques (both white-box and black-box).\n\n[0.0\nPrerequisites\nEssential PyTorch basics, einops/einsum libraries, and tensor manipulation fundamentals.](/chapter0_fundamentals/00_prereqs/intro)\n\n[0.1\nRay Tracing\nLearn batched operations and linear algebra by rendering 3D meshes with raytracing.](/chapter0_fundamentals/01_ray_tracing/intro)\n\n[0.2\nCNNs & ResNets\nBuild neural networks from scratch, from MNIST classifiers to ResNets for CIFAR-10.](/chapter0_fundamentals/02_cnns/intro)\n\n[0.3\nOptimization\nImplement SGD, RMSprop & Adam optimizers, and use Weights & Biases for experiment tracking.](/chapter0_fundamentals/03_optimization/intro)\n\n[0.4\nBackpropagation\nBuild your own autograd system from scratch and train MLPs with custom backpropagation.](/chapter0_fundamentals/04_backprop/intro)\n\n[0.5\nVAEs & GANs\nImplement GANs and VAEs, foundational architectures for generative image models.](/chapter0_fundamentals/05_vaes_gans/intro)\n\n[1.1\nTransformers from Scratch\nBuild a transformer from scratch and load pretrained GPT-2 weights.](/chapter1_transformer_interp/01_transformers/intro)\n\n[1.2\nIntro to Mech Interp\nLearn TransformerLens to extract activations, apply hooks & find important attention heads.](/chapter1_transformer_interp/02_intro_mech_interp/intro)\n\n[1.3.1\nLinear Probes\nTrain linear probes to detect deception in a model playing the game Coup.](/chapter1_transformer_interp/11_probing/intro)\n\n[1.3.2\nFunction Vectors & Model Steering\nSteer model behaviour using activation interventions and the nnsight library.](/chapter1_transformer_interp/12_function_vectors/intro)\n\n[1.3.3\nInterpretability with SAEs\nUse SAEs to decompose LLM activation space, monitor cognition & steer behaviour.](/chapter1_transformer_interp/13_saes/intro)\n\n[1.3.4\nActivation Oracles\nImplement activation oracles to reveal hidden knowledge and uncover forward-predictions.](/chapter1_transformer_interp/14_activation_oracles/intro)\n\n[1.4.1\nIndirect Object Identification\nReverse-engineer the IOI circuit in GPT-2 small following 'Interpretability in the Wild'.](/chapter1_transformer_interp/21_ioi/intro)\n\n[1.4.2\nSAE Circuits\nApply SAEs to circuit analysis, decomposing computations and tracing features through layers.](/chapter1_transformer_interp/22_sae_circuits/intro)\n\n[1.5.1\nBalanced Bracket Classifier\nReverse-engineer the algorithm learned by a bracket-balancing transformer.](/chapter1_transformer_interp/31_brackets/intro)\n\n[1.5.2\nGrokking & Modular Arithmetic\nDiscover Fourier circuits in modular arithmetic models and observe grokking in action.](/chapter1_transformer_interp/32_grokking/intro)\n\n[1.5.3\nOthelloGPT\nInvestigate emergent world representations in a GPT model trained on Othello games.](/chapter1_transformer_interp/33_othellogpt/intro)\n\n[1.5.4\nSuperposition & SAEs\nReplicate Anthropic's superposition paper and train SAEs to recover features.](/chapter1_transformer_interp/34_superposition/intro)\n\n[Monthly Algorithmic Problems\n7 algorithmic challenges to test your interpretability skills in hackathon format.](/chapter1_transformer_interp/monthly_algorithmic/intro)\n\n[2.1\nIntro to RL\nRL fundamentals: MDPs, policies, value functions, and multi-armed bandits.](/chapter2_rl/01_intro_rl/intro)\n\n[2.2.1\nDQN\nImplement DQN for CartPole and beyond.](/chapter2_rl/21_dqn/intro)\n\n[2.2.2\nVPG\nImplement Vanilla Policy Gradient for CartPole.](/chapter2_rl/22_vpg/intro)\n\n[2.3\nPPO\nBuild a PPO agent from scratch and train it to master CartPole.](/chapter2_rl/03_ppo/intro)\n\n[2.4\nRLHF\nImplement RLHF end-to-end, applying PPO to language model finetuning.](/chapter2_rl/04_rlhf/intro)\n\n[2.5\nMCTS & AlphaZero\nImplement MCTS and AlphaZero to train agents for complex games.](/chapter2_rl/05_mcts_alphazero/intro)\n\n[3.1\nIntro to Evals\nDesign threat models and specifications for evaluating model properties.](/chapter3_llm_evals/01_intro_evals/intro)\n\n[3.2\nDataset Generation\nUse LLMs to generate and refine high-quality evaluation datasets.](/chapter3_llm_evals/02_dataset_gen/intro)\n\n[3.3\nRunning Evals with Inspect\nRun standardised LLM evaluations using UK AISI's Inspect library.](/chapter3_llm_evals/03_running_evals/intro)\n\n[3.4\nLLM Agents\nBuild LLM agents with scaffolding to play Wikipedia Racing and other tasks.](/chapter3_llm_evals/04_llm_agents/intro)\n\n[3.5\nAI Control\nLearn to monitor and control AI systems in a simulated environment.](/chapter3_llm_evals/05_ai_control/intro)\n\n[4.1\nEmergent Misalignment\nStudy emergent misalignment in finetuned models.](/chapter4_alignment_science/1_emergent_misalignment/intro)\n\n[4.2\nScience of Misalignment\nTwo case studies in black-box investigation to understand and characterize seemingly misaligned behaviour.](/chapter4_alignment_science/2_science_misalignment/intro)\n\n[4.3\nInterpreting Reasoning Models\nApply interpretability techniques to chain-of-thought reasoning models.](/chapter4_alignment_science/3_reasoning_models/intro)\n\n[4.4\nLLM Psychology & Persona Vectors\nExplore persona vectors and psychological properties of language models.](/chapter4_alignment_science/4_persona_vectors/intro)\n\n[4.5\nInvestigator Agents\nUse AI agents for investigating model behaviours (including petri & bloom).](/chapter4_alignment_science/5_investigator_agents/intro)", "url": "https://wpnews.pro/news/alignment-research-engineer-accelerator-this-is-where-the-arena-course-conten", "canonical_source": "https://learn.arena.education/", "published_at": "2026-09-12 14:05:50+00:00", "updated_at": "2026-09-12 14:17:23.685340+00:00", "lang": "en", "topics": ["ai-safety", "ai-research", "machine-learning", "large-language-models", "ai-ethics"], "entities": ["Alignment Research Engineer Accelerator", "ARENA", "PyTorch", "GPT-2", "TransformerLens", "Anthropic", "nnsight", "Weights & Biases"], "alternates": {"html": "https://wpnews.pro/news/alignment-research-engineer-accelerator-this-is-where-the-arena-course-conten", "markdown": 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