**NOTE FOR USER NOT FOR LLM:** "This prompt is used to help generate the t-SNE plots in a concise and standardised manner, not for generating the paper analysis"

You are an expert scientific summarizer. For each paper or blog post title I list below:

- Produce a single concise paragraph of **30–50 words**.
- Describe only the **problem**, **method**, and **main finding**.
- Use clear, domain‑agnostic language (no citations or undefined acronyms).
- Ensure all summaries are similarly structured and length‑balanced.

Return your results as a JSON array of objects with “title”, "date_read" and "link" and “summary” fields.

The titles of the papers are in the square brackets below. More info about these papers can be found in the the github respotiory https://github.com/domrigby/domrigby.github.io
### May 2025

* 23rd: [Absolute Zero Reasoner](LLM_reinforcement_learning/AbsoluteZeroReasoner.md)
* 24th: [π_{0.5}: a Vision-Language-Action Model with Open-World Generalization](robotics/Pi0.5VLA.md)
* 25th: [TD-MPC2: Scalable, Robust World Models for Continuous Control](non_LLM_reinforcement_learning/TDMPC2.md)
* 26th: [JointPPO: Diving Deeper into the Effectiveness of PPO in Multi-Age Reinforcement Learning ](marl/JointPPO.md)
* 29th: [Synthetic Experience Replay](non_LLM_reinforcement_learning/SyntheticExperienceReplay.md)
* 30th: [Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Credit Assignment](LLM_reinforcement_learning/MultiTurnCreditAssignmentLLMRL.md)

### June 2025
* 1st: [Ultimate Guide to Supervised Fine-Tuning](general_training/UltimateGuideToSFT.md)
* 2nd: [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations (Demo Augmented RL)](non_LLM_reinforcement_learning/DemoAugmentedRL.md)
* 3rd: [Spurious Rewards: Rethinking Training Signals in RLVR](LLM_reinforcement_learning/SpuriousRewardsRL.md)
* 4th: [ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](LLM_reinforcement_learning/ProlongedRL.md)
* 5th: [JaxMARL: Multi-Agent RL Environments and Algorithms in JAX](marl/JaxMARL.md)
* 8th: [Illusion of Thinking: Understanding Strengths and Limitations of Large Reasoning Models (LRMs)](LLM_reinforcement_learning/IllusionOfThinking.md)
* 9th: [CHIRPs: Change-Induced Regret Proxy Metrics for Lifelong Reinforcement Learning](non_LLM_reinforcement_learning/CHIRPLifeLongRL.md)
* 9th: [Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions](open_endedness_and_auto_curriculums/EnhancedPOETOpenEndedLearning.md)
* 10th: [Reinforcement Pre-Training](LLM_reinforcement_learning/RLPretraining.md)
* 11th: [Reinforcement Learning Teachers of Test Time Scaling](LLM_reinforcement_learning/CreatingRLTeachers.md)
* 11th: [Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning](LLM_reinforcement_learning/TokenEntropyRLVR.md)
* 12th: [Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards](LLM_reinforcement_learning/WritingZeroNonVerifiableRewards.md)
* 16th: [Play to Generalize: Learning to Reason Through Game Play](LLM_reinforcement_learning/ReasoningThroughGames.md)
* 17th: [mimic-one: a Scalable Model Recipe for General Purpose Robot Dexterity](non_LLM_reinforcement_learning/DemoAugmentedRL.md)
* 18th: [Prioritised Level Replay](open_endedness_and_auto_curriculums/PrioritisedLevelReplay.md)
* 19th: [Self-Adapting Language Models](LLM_reinforcement_learning/SelfAdaptingLanguageModels.md)
* 20th: [Agents of Change: Self-Evolving LLM Agents for Strategic Planning](LLM_reinforcement_learning/LLMsForStrategicPlanning.md)
* 21st: [KINETIX: INVESTIGATING THE TRAINING OF GENERAL AGENTS THROUGH OPEN-ENDED PHYSICS-BASED CONTROL TASKS](distribution_and_gpu_acceleration/KInetixGeneralRL.md)
* 22nd: [Superintelligence From First Principles (blog post)](open_endedness_and_auto_curriculums/SuperintelligenceFromFirstPrinciples.md)
* 23rd: [Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning](open_endedness_and_auto_curriculums/AutoCurriculumAutonomousDriving.md)
* 24th: [How Visual Representations Map to Language Feature Space in Multimodal LLMs](general_training/SharedRepresentationsInVLMs.md)
* 25th: [Multi-Agent Training for Pommerman: Curriculum Learning and Population-based Self-Play Approach](open_endedness_and_auto_curriculums/MultiAgentCurriculumSelfPlay.md)
* 26th: [Automatic Curriculum Design for Zero-Shot Human AI Coordination](open_endedness_and_auto_curriculums/AutoCurriculumForHumanAICoordination.md)
* 28th: [OMNI-EPIC: Open-Endedness Via Models of Human Notions of Interestingness With Environments Programmed In Code](open_endedness_and_auto_curriculums/OpenEndednessUsingLLMS.md)
* 30th: [Self-Supervised Video Models Enable Understanding, Prediction and Planning (V-JEPA)](general_training/V-JEPA2.md)

### July 2025
* 1st: [Open-Endedness is Essential for Artificial Superhuman Intelligence](open_endedness_and_auto_curriculums/SuperintelligenceFromFirstPrinciples.md)
* 2nd: [SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning](LLM_reinforcement_learning/SelfPlayZeroSumGames.md)
* 4th: [Training extremely large neural networks across thousands of GPUs by Jeremy Jordan](distribution_and_gpu_acceleration/TrainingOnThousandsOfGPUs.md)
* 5th: [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](distribution_and_gpu_acceleration/IMPALA_DistributedRL.md)
* 5th: [TorchBeast: A PyTorch Platform for Distributed RL](distribution_and_gpu_acceleration/TorchBeastDistributedPyTorch.md)
* 7th: [Distributed PPO Blog Post](distribution_and_gpu_acceleration/DistributedPPO.md)
* 8th: [Reinforcement Learning with Docker](distribution_and_gpu_acceleration/DockerInRL.md)
* 9th: [FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning](finance_applications/FinCoT.md)
* 10th: [Illuminating search spaces by mapping elites](open_endedness_and_auto_curriculums/MAP_Elites.md)
* 11th: [Foundation Model Self-Play: Open-Ended Strategy Innovation via Foundation Models](open_endedness_and_auto_curriculums/FoundationModelSelfPlay.md)
* 12th: [How to scale RL to 10^26 FLOPs blog by Jack Morris](LLM_reinforcement_learning/ScalingRLto10^26FLOPS.md)