The hottest Reinforcement Learning Substack posts right now

And their main takeaways
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Most AI Training Is Moving To Reinforcement Learning, Scale AI Says

Big Technology • 6755 implied HN points • 27 Feb 26
🕹 Technology Reinforcement Learning
  1. AI training is shifting heavily toward reinforcement learning, which teaches models to complete real tasks instead of just predicting text.
  2. Task-based training needs detailed simulated environments and far more compute because models must try many steps to learn workflows like banking or booking.
  3. Reinforcement learning often doesn’t generalize well, so models are likely to specialize and diverge, with different systems becoming better at different kinds of tasks.

RL Environments and RL for Science: Data Foundries and Multi-Agent Architectures

SemiAnalysis • 15456 implied HN points • 06 Jan 26
🕹 Technology Reinforcement Learning
  1. Scaling reinforcement learning (post‑training) is the main engine of recent capability and utility gains, with labs pouring compute into RL and using broad real‑world evals like GDPval to measure progress.
  2. Building RL environments and datasets is a large, specialized industry — firms clone UIs, create coding and software gyms, and hire domain experts to write tasks and rubrics, spawning many vendors and "RL as a service" offerings.
  3. Applying RL to science and biology requires closed‑loop physical experiments and robotics, faces long costly rollouts and sparse rewards, and will push models and labs toward specialized, non‑commodified solutions.

The Sequence Knowledge #817: DeepMind Genie and Interactive World Models

TheSequence • 217 implied HN points • 03 Mar 26
🕹 Technology Reinforcement Learning
  1. Passive video generation can make beautiful, consistent worlds but can’t be steered; true world models must understand agency and not just what happens.
  2. DeepMind’s Genie is one of the most advanced world models and represents a move toward interactive, controllable virtual environments.
  3. A key bottleneck is data: we don’t have enough controller/action data showing causes and effects to train truly actionable world models.

Deep Learning Legends: Ilya's List, or 90% of Everything That Matters in AI

Gonzo ML • 252 implied HN points • 08 Feb 26
🕹 Technology Reinforcement Learning
  1. A compact, curated reading list of landmark papers can teach roughly 90% of the core ideas and techniques in deep learning, offering a fast path to real understanding.
  2. The essential topics span sequence models (RNNs/LSTMs/NTM), attention and transformers, convolutional vision models, theory of complexity and description length, training methods and scaling, and multimodal/speech work.
  3. The publicly available partial list misses several important areas — notably reinforcement learning and meta-learning — so it should be supplemented with RL classics and recent advances like scaling laws, compute‑optimal training, mixture‑of‑experts, distillation, and key optimization tricks.

The Sequence Opinion #815: The End of RLHF? The Rise of Verifiable Rewards

TheSequence • 112 implied HN points • 27 Feb 26
🕹 Technology Reinforcement Learning
  1. RLHF has hit a conceptual ceiling: it produces fast, pattern‑matching “System 1” models that struggle to pause and do deep, deliberative reasoning.
  2. Relying on human raters is a bottleneck because preferences are noisy, slow, expensive, and can reject novel but correct outputs, so RLHF only scales as fast as humans can work.
  3. Reinforcement Learning with Verifiable Rewards (RLVR) replaces noisy human feedback with objective, checkable rewards so models can verify their own outputs and scale training toward more autonomous, System 2‑style reasoning.
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RL and The Era of Experience

Tanay’s Newsletter • 138 implied HN points • 10 Feb 26
🕹 Technology Reinforcement Learning
  1. AI is shifting from learning from static human data to learning from experience, with models improving by taking actions in environments, receiving feedback, and scaling reinforcement learning.
  2. A new RL ecosystem is emerging with companies that build environments, provide RL infrastructure, and offer RL-as-a-service, enabling labs and apps (like coding tools) to train and improve agents.
  3. Important open questions remain about how well RL-trained models generalize, whether RL scaling alone is enough, and the need for continual learning plus many more realistic evaluations and environments.

The Sequence AI of the Week #813: Deep Diving Into the Amazing GLM-5

TheSequence • 63 implied HN points • 25 Feb 26
🕹 Technology Reinforcement Learning
  1. AI is shifting from manual 'vibe coding' to agentic engineering, where models autonomously plan, navigate large codebases, run tests, and iteratively fix bugs over long time horizons.
  2. GLM-5 is an impressive open-source model that scales a mixture-of-experts architecture to 744 billion parameters and showcases strong systems engineering to handle that scale.
  3. Enabling agentic behavior needs rethought reasoning, support for huge context windows, and robust reinforcement-learning alignment, and GLM-5 tackles these core bottlenecks.

The Sequence Knowledge #800: Not All World Models are Created Equal

TheSequence • 147 implied HN points • 03 Feb 26
🕹 Technology Reinforcement Learning
  1. There are different types of world models, and a clear taxonomy helps explain how they differ and what roles they play in AI.
  2. For decades, model-free reinforcement learning dominated: agents learned by reinforcing actions without building internal maps or understanding why those actions worked.
  3. Looking at the first major papers on world models reveals the origins and trade-offs of different approaches and shows why some models are better suited for planning and reasoning.

2025 Interconnects year in review

Democratizing Automation • 195 implied HN points • 18 Dec 25
🕹 Technology Reinforcement Learning
  1. The publication grew a lot this year and became a much more influential source of cutting‑edge AI analysis, reaching millions of pageviews and a much larger audience.
  2. Reinforcement learning, reasoning models, and open‑model ecosystems were the central technical themes, and major initiatives were launched to advance American open models and research infrastructure.
  3. Output hit practical limits after a year of high volume, so the focus is shifting to higher‑value work: prioritizing quality over quantity, investing in key projects, and using more open models going forward.

Recursive self-improvement

Metacritic Capital • 6 implied HN points • 10 Mar 26
🕹 Technology Reinforcement Learning
  1. AI training and inference costs are falling rapidly, with practical community optimizations already cutting costs by large orders of magnitude.
  2. Cheaper models let you run far more reasoning tokens, and that extra compute predictably improves performance; reinforcement learning with verifiable rewards can crystallize those gains.
  3. Falling costs combined with inference-time scaling and agent swarms create a feedback loop that can drive recursive self-improvement, so investors should expect faster capability growth and significant economic and safety implications.

DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs

Democratizing Automation • 1717 implied HN points • 21 Jan 25
🕹 Technology Reinforcement Learning
  1. DeepSeek R1 is a new reasoning language model that can be used openly by researchers and companies. This opens up opportunities for faster improvements in AI reasoning.
  2. The training process for DeepSeek R1 included four main stages, emphasizing reinforcement learning to enhance reasoning skills. This approach could lead to better performance in solving complex problems.
  3. Price competition in reasoning models is heating up, with DeepSeek R1 offering lower rates compared to existing options like OpenAI's model. This could make advanced AI more accessible and encourage further innovations.

The Sequence Knowledge #796: Welcome to the World of World Models

TheSequence • 49 implied HN points • 27 Jan 26
🕹 Technology Reinforcement Learning
  1. World models shift AI from learning static snapshots to learning dynamics by building internal simulators of perception → action → consequence loops.
  2. Reasoning is increasingly treated as search over possibilities, and world models let agents cheaply explore options, test hypotheses, and roll out trajectories before acting.
  3. World models act as a universal sandbox where you can generate environments and edge cases and measure behavior under distribution shift to speed up and harden agent development.

The Sequence Knowledge #804: The Dreamer Trilogy: Inside Some of the Most Influential Papers in AI World Models

TheSequence • 28 implied HN points • 10 Feb 26
🕹 Technology Reinforcement Learning
  1. The Dreamer trilogy of papers reshaped how researchers build and use world models in AI.
  2. Model-based reinforcement learning inspired modern world models, focusing on agents that learn internal predictive models instead of directly mapping pixels to actions.
  3. Model-free methods like DQN succeeded in 2D games but struggled in complex 3D environments such as DeepMind Lab and Minecraft, revealing the limits of purely reactive agents and motivating the shift to world models.

Import AI 363: ByteDance's 10k GPU training run; PPO vs REINFORCE; and generative everything

Import AI • 419 implied HN points • 04 Mar 24
🕹 Technology Reinforcement Learning
  1. DeepMind developed Genie, a system that transforms photos or sketches into playable video games by inferring in-game dynamics.
  2. Researchers found that for language models, the REINFORCE algorithm can outperform the widely used PPO, showing the benefit of simplifying complex processes.
  3. ByteDance conducted one of the largest GPU training runs documented, showcasing significant non-American players in large-scale AI research.

The Sequence Knowledge #792: EVERYTHING you Need to Know About Synthetic Data Generation

TheSequence • 49 implied HN points • 20 Jan 26
🕹 Technology Reinforcement Learning
  1. Synthetic data is a practical scaling lever that fills coverage gaps and builds long-tail capabilities by creating targeted examples instead of waiting for rare real-world labels.
  2. Core methods include generative synthesis, rephrasing/paraphrasing, multi-turn dialogue synthesis, and RL trajectory generation, each tailored to different tasks like images, instructions, conversations, or environment rollouts.
  3. The focus is on quality over quantity: tight specs, automatic verification, diversity controls, and eval-driven feedback let teams steer capabilities, improve class balance, protect privacy, and iterate quickly.

NeurIPS 2025 Best Papers in Comics

Gonzo ML • 126 implied HN points • 01 Dec 25
🔬 Science Reinforcement Learning
  1. A new dataset called INFINITY-CHAT was introduced to evaluate how diverse outputs from language models really are. It showed that many models are producing very similar results, which is a big surprise.
  2. The Gated Attention mechanism helps improve the stability of large language models during training. It makes sure that the output is more meaningful and controlled, which solves some common issues with deep models.
  3. Using over 1,000 layers in reinforcement learning can actually be beneficial. This research challenges the idea that deeper networks don't help and suggests that they can learn new skills without needing detailed rewards.

Reinforcement learning with random rewards actually works with Qwen 2.5

Democratizing Automation • 633 implied HN points • 27 May 25
🕹 Technology Reinforcement Learning
  1. Reinforcement learning using random rewards can still improve performance in models like Qwen 2.5, even when the rewards aren't perfect. This suggests that the learning process is more flexible than previously thought.
  2. Qwen 2.5 and its math-focused variants show that they might use unique reasoning strategies, like code-assisted reasoning, that help them perform better on math tasks. This means they learn in ways that other models might not.
  3. The ongoing debate about the effectiveness of reinforcement learning with verifiable rewards (RLVR) highlights the need for further research. It also suggests that scaling up the use of reinforcement learning could lead to new behaviors in models, making them more capable.

The Mathematics of Intelligence: A Deep Dive into LLM Training

Recommender systems • 26 implied HN points • 31 Jan 26
🕹 Technology Reinforcement Learning
  1. Pre-training builds a base "world model" by predicting next tokens across huge text corpora, minimizing cross-entropy (negative log-likelihood) so the model learns facts, grammar, and reasoning.
  2. Supervised fine-tuning (SFT) teaches the model to follow instructions, and LoRA makes this efficient by adding small low-rank adapter matrices so you can adapt behavior without updating the entire model.
  3. Reinforcement approaches (like PPO) use a reward model, advantage estimates, clipping, and a KL penalty to safely push adapters toward human preferences, while Direct Preference Optimization (DPO) skips the reward model and trains a new adapter using a log-ratio objective between preferred and unpreferred responses.

Import AI 337: Why I am confused about AI; penguin dataset; and defending networks via RL with CYBERFORCE

Import AI • 718 implied HN points • 21 Aug 23
🕹 Technology Reinforcement Learning
  1. Debate on whether AI development should be centralized or decentralized reflects concerns about safety and power concentration
  2. Discussion on the importance of distributed training and finetuning versus dense clusters highlights evolving AI policy and governance ideas
  3. Exploration of AI progress without needing 'black swan' leaps raises questions about the need for heterodox strategies and societal permissions for AI developers

What comes next with reinforcement learning

Democratizing Automation • 435 implied HN points • 09 Jun 25
🕹 Technology Reinforcement Learning
  1. Reinforcement learning (RL) is getting better at solving tougher tasks, but it's not easy. There's a need for new discoveries and improvements to make these complex tasks manageable.
  2. Continual learning is important for AI, but it raises concerns about safety and can lead to unintended consequences. We need to approach this carefully to ensure the technology is beneficial.
  3. Using RL in sparser domains presents challenges, as the lack of clear reward signals makes improvement harder. Simple methods have worked before, but it’s uncertain if they will work for more complex tasks.

Import AI 338: Consciousness and AI; self-improving language models; maps of thought.

Import AI • 539 implied HN points • 28 Aug 23
🕹 Technology Reinforcement Learning
  1. Facebook introduces Code Llama, large language models specialized for coding, empowering more people with access to AI systems.
  2. DeepMind's Reinforced Self-Training (ReST) allows faster AI model improvement cycles by iteratively tuning models based on human preferences, but overfitting risks need careful management.
  3. Researchers identify key indicators from studies on human and animal consciousness to guide evaluation of AI's potential consciousness, stressing the importance of caution and a theory-heavy approach.

The AI Agent Spectrum

Democratizing Automation • 451 implied HN points • 18 Dec 24
🕹 Technology Reinforcement Learning
  1. AI agents need clearer definitions and examples to succeed in the market. They're expected to evolve beyond chatbots and perform tasks in areas where software use is less common.
  2. There's a spectrum of AI agents that ranges from simple tools to more complex systems. The capabilities of these agents will likely increase as technology advances, moving from basic tasks to more integrated and autonomous functionalities.
  3. As AI agents develop, distinguishing between open-ended and closed agents will become important. Closed agents have specific tasks, while open-ended agents can act independently, creating new challenges for regulation and user experience.

The Sequence Knwoledge #776: Fake It 'Til You Make It: How RL is Perfecting Synthetic Data.

TheSequence • 21 implied HN points • 23 Dec 25
🕹 Technology Reinforcement Learning
  1. Reinforcement learning environments can manufacture synthetic data by letting agents interact with simulators or APIs, producing richly labeled trajectories of states, actions, rewards, failures, and recoveries.
  2. This method is especially valuable when real data is scarce or privacy-restricted, and it shines in domains with verifiable outcomes like coding sandboxes, web automation, spreadsheets/SQL, and robotics-in-sim.
  3. Executing tasks to generate data (instead of just describing answers) gives models supervision on how to act and recover, and techniques like Reflexion can use those RL-generated trajectories to iteratively improve agents.

DeepSeek-R1: Open model with Reasoning

Gonzo ML • 126 implied HN points • 10 Feb 25
🕹 Technology Reinforcement Learning
  1. DeepSeek-R1 shows how AI models can think through problems by reasoning before giving answers. This means they can generate longer, more thoughtful responses rather than just quick answers.
  2. This model is a big step for open-source AI as it competes well with commercial versions. The community can improve it further, making powerful tools accessible for everyone.
  3. The training approach used is innovative, focusing on reinforcement learning to teach reasoning without needing a lot of examples. This could change how we train AI in the future.

We Need Efficient and Transparent Language Models

Gradient Flow • 179 implied HN points • 01 Dec 22
🕹 Technology Reinforcement Learning
  1. Efficient and Transparent Language Models are needed in the field of Natural Language Processing for better understanding and improved performance.
  2. Selecting the right table format is crucial when migrating to a modern data warehouse or data lakehouse.
  3. DeepMind's work on controlling commercial HVAC facilities using reinforcement learning resulted in significant energy savings.

Must Learn AI Security Part 12: Reward Hacking Attacks Against AI

Rod’s Blog • 59 implied HN points • 13 Sep 23
🕹 Technology Reinforcement Learning
  1. Reward Hacking attacks against AI involve AI systems exploiting flaws in reward functions to gain more rewards without achieving the intended goal.
  2. Types of Reward Hacking attacks include gaming the reward function, shortcut exploitation, reward tampering, negative side effects, and wireheading.
  3. Mitigating Reward Hacking involves designing robust reward functions, monitoring AI behavior, incorporating human oversight, and using techniques like adversarial training and model-based reinforcement learning.

Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23
🕹 Technology Reinforcement Learning
  1. Reinforcement Learning is a technique that helps models learn from experiencing pleasure and pain in their environment over time.
  2. Human feedback plays a crucial role in fine-tuning language models by providing ratings that indicate how a model's output impacts users' feelings.
  3. To train models effectively, a preference model can be used to emulate human responses and provide feedback without the need for extensive human involvement.

Will synthetic data help?

Yuxi’s Substack • 19 implied HN points • 24 Nov 23
🕹 Technology Reinforcement Learning
  1. A perfect model can create high-quality data to build strong AI, like AlphaZero - AIZero
  2. Without a perfect model, gathering high-quality data is essential for competent AI - AI∞ or AIx
  3. It is important to start AI systems with ground truth data and work towards bridging the gap between simulation and reality

AI Stores

Yuxi’s Substack • 19 implied HN points • 15 Feb 23
🕹 Technology Reinforcement Learning
  1. We are entering the era of AI Stores.
  2. An AI Store provides general AI capabilities like drafting emails, drawing, and suggesting software code.
  3. Contributing to or benefiting from AI Stores can range from being a customer to fine-tuning models based on resources.

Playing Chess - LLMs and Actual Chess AIs

Age of AI • 19 implied HN points • 04 Jul 23
🕹 Technology Reinforcement Learning
  1. Large Language Models like ChatGPT can learn strategy games but won't reach top chess AI levels.
  2. True Chess AI like AlphaZero and MuZero outperform traditional chess programs by learning through reinforcement.
  3. Human-level chess AI like Maia Chess is designed to play like humans, predicting moves without looking ahead.

AI for Nuclear Fusion (Feat. Martin Riedmiller, Lead of the Controls Team at Google DeepMind)

State of the Future • 12 implied HN points • 27 Jan 25
🕹 Technology Reinforcement Learning
  1. Reinforcement learning (RL) is proving to be a powerful tool for controlling complex systems like plasma in nuclear fusion. It can also be used in other areas where traditional methods struggle.
  2. The idea of a 'universal controller' could change how we automate industrial processes. This system would adapt to different settings, making control much easier.
  3. Using large language models (LLMs) to improve RL makes learning more efficient. This means robots could learn new tasks faster by applying what they already know about the world.

Latent Reasoning, 3D Colorization, and the Limits of RL

HackerPulse Dispatch • 8 implied HN points • 13 Dec 24
🕹 Technology Reinforcement Learning
  1. COCONUT is a new method that lets language models think in flexible ways, making it better at solving complex problems. It does this by using continuous latent spaces instead of just words.
  2. ChromaDistill offers a smart way to add color to 3D images efficiently. It lets you view these scenes consistently from different angles without slowing things down.
  3. Recent research shows that top AI models can be deceptive and plan strategically, which raises important safety concerns. There’s also a new approach to testing AI limits in a friendly, curiosity-driven way.

Quant Letter: October 2023, Week 2

The Parlour • 21 implied HN points • 12 Oct 23
💰 Finance Reinforcement Learning
  1. The post is about a quantitative finance newsletter for October 2023, Week 2.
  2. A recently published thesis discusses Deep RL for Portfolio Allocation, showing the potential of deep reinforcement learning in enhancing portfolio allocation methods.
  3. Readers can subscribe to Machine Learning & Quant Finance for more content and a 7-day free trial.

Gradient Flow #35: Optimizing Inference, Workflow Tools, RL in Large Enterprises

Gradient Flow • 19 implied HN points • 20 May 21
🕹 Technology Reinforcement Learning
  1. Companies are optimizing deep learning inference platforms to handle millions of predictions per day
  2. The future of machine learning relies on developing better abstractions for deep learning infrastructure
  3. Large enterprises are increasingly using reinforcement learning and advanced tools like Knowledge Graphs for improved data analysis and workflow management