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54,302 blogs · [ { "id": "01a0df50-42a2-7315-a56c-0dc7be96fcbc", "title": "Reinforcement Learning for LLMs: The Complete Guide", "url": "https://cameronrwolfe.substack.com/p/llm-rl", "published_at": "2026-08-24T09:33:46+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7beee0adb", "title": "Notes on Midtraining", "url": "https://cameronrwolfe.substack.com/p/midtraining-notes", "published_at": "2026-08-10T09:33:22+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7befd0d1e", "title": "Agentic World Models", "url": "https://cameronrwolfe.substack.com/p/agentic-world-models", "published_at": "2026-07-20T09:33:09+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7bfb445ed", "title": "Agentic RL: Frameworks and Best Practices", "url": "https://cameronrwolfe.substack.com/p/agentic-rl", "published_at": "2026-06-22T09:33:53+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c0406aca", "title": "Agent Evaluation: A Detailed Guide", "url": "https://cameronrwolfe.substack.com/p/agent-evals", "published_at": "2026-05-18T09:33:28+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c094fe0e", "title": "RL Scaling Laws for LLMs", "url": "https://cameronrwolfe.substack.com/p/rl-scaling-laws", "published_at": "2026-04-20T09:33:44+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c0c92ba0", "title": "The Anatomy of an LLM Benchmark", "url": "https://cameronrwolfe.substack.com/p/llm-bench", "published_at": "2026-03-30T09:33:10+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c1ac5027", "title": "Applying Statistics to LLM Evaluations", "url": "https://cameronrwolfe.substack.com/p/stats-llm-evals", "published_at": "2026-03-09T09:33:37+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c21cd99d", "title": "Rubric-Based Rewards for RL", "url": "https://cameronrwolfe.substack.com/p/rubric-rl", "published_at": "2026-02-16T10:33:41+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c25922e4", "title": "Continual Learning with RL for LLMs", "url": "https://cameronrwolfe.substack.com/p/rl-continual-learning", "published_at": "2026-01-26T10:33:14+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c291e750", "title": "GRPO++: Tricks for Making RL Actually Work", "url": "https://cameronrwolfe.substack.com/p/grpo-tricks", "published_at": "2026-01-05T10:33:50+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c315cb2f", "title": "Olmo 3 and the Open LLM Renaissance", "url": "https://cameronrwolfe.substack.com/p/olmo-3", "published_at": "2025-12-15T10:33:23+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c375b251", "title": "Group Relative Policy Optimization (GRPO)", "url": "https://cameronrwolfe.substack.com/p/grpo", "published_at": "2025-11-24T10:33:31+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c4231ca3", "title": "PPO for LLMs: A Guide for Normal People", "url": "https://cameronrwolfe.substack.com/p/ppo-llm", "published_at": "2025-10-27T09:33:23+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c43ca585", "title": "REINFORCE: Easy Online RL for LLMs", "url": "https://cameronrwolfe.substack.com/p/reinforce", "published_at": "2025-09-29T09:33:23+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c47e2e2d", "title": "Online versus Offline RL for LLMs", "url": "https://cameronrwolfe.substack.com/p/online-rl", "published_at": "2025-09-08T09:33:21+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c56fcc6b", "title": "GPT-oss from the Ground Up", "url": "https://cameronrwolfe.substack.com/p/gpt-oss", "published_at": "2025-08-18T09:33:11+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c5be1245", "title": "Direct Preference Optimization (DPO)", "url": "https://cameronrwolfe.substack.com/p/direct-preference-optimization", "published_at": "2025-07-28T09:33:20+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c660c225", "title": "Reward Models", "url": "https://cameronrwolfe.substack.com/p/reward-models", "published_at": "2025-06-30T09:33:16+00:00" }, { "id": "01a0df50-42a2-7315-a56c-0dc7c6ffc35b", "title": "AI Agents from First Principles", "url": "https://cameronrwolfe.substack.com/p/ai-agents", "published_at": "2025-06-09T09:33:09+00:00" } ] posts Claim your blog
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cameronrwolfe.substack.com

2026

Reinforcement Learning for LLMs: The Complete Guideoriginal ↗
24 Aug 2026
Notes on Midtrainingoriginal ↗
10 Aug 2026
Agentic World Modelsoriginal ↗
20 Jul 2026
Agentic RL: Frameworks and Best Practicesoriginal ↗
22 Jun 2026
Agent Evaluation: A Detailed Guideoriginal ↗
18 May 2026
RL Scaling Laws for LLMsoriginal ↗
20 Apr 2026
The Anatomy of an LLM Benchmarkoriginal ↗
30 Mar 2026
Applying Statistics to LLM Evaluationsoriginal ↗
9 Mar 2026
Rubric-Based Rewards for RLoriginal ↗
16 Feb 2026
Continual Learning with RL for LLMsoriginal ↗
26 Jan 2026
GRPO++: Tricks for Making RL Actually Workoriginal ↗
5 Jan 2026

2025

Olmo 3 and the Open LLM Renaissanceoriginal ↗
15 Dec 2025
Group Relative Policy Optimization (GRPO)original ↗
24 Nov 2025
PPO for LLMs: A Guide for Normal Peopleoriginal ↗
27 Oct 2025
REINFORCE: Easy Online RL for LLMsoriginal ↗
29 Sept 2025
Online versus Offline RL for LLMsoriginal ↗
8 Sept 2025
GPT-oss from the Ground Uporiginal ↗
18 Aug 2025
Direct Preference Optimization (DPO)original ↗
28 Jul 2025
Reward Modelsoriginal ↗
30 Jun 2025
AI Agents from First Principlesoriginal ↗
9 Jun 2025