Brilliant work by @ArminPCM and our research team on the latest RL Environments themed blog. Dive in
MACHINE LEARNING
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Building Advanced RL Environments for Reliable AI Reasoning
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A good RL environment is more than a sandbox — it’s a system with:
– Real tools & APIs
– Stateful feedback
– Deterministic rewards & rubrics At Snorkel, we combine expert-built tasks + automated QC to create environments that truly measure reasoning and reliability. -
Snorkel Builds Enterprise-Grade Reinforcement Learning Environments
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New from @ArminPCM : how Snorkel builds reinforcement learning (RL) environments that train and evaluate agents in realistic, enterprise-grade settings.
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Tokenizer Approaches Impact LLM Performance on HellaSwag Benchmarks
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If you measure downstream performance on HellaSwag rather than speedrun-equivalent loss, then different tokenizer approaches come out on top… The first run I did was much better on common-sense downstream, trained in equivalent time or better.
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Tokenizer Training and Data Filtering Compliance Standards
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Well, the tokenizer I used was trained on large quantities of data — I filtered the tokens based on yet more data from FineWeb. Question is if that's acceptable according to your rules…
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AI Improves Grid Optimization and Predictive Maintenance Reliability
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Some early progress can be seen in grid optimisation and predictive maintenance, where AI is improving reliability, reducing downtime, and creating a more intelligent use of resources.
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Celebrating shift from JSON tool calls to code blobs
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Finally! Congrats, a step in a great direction!
We've been stuck in this local optimum of "writing actions as JSON blobs with individual tool calls in them" for much too long. Just let models write tool calls in code blobs! -
ThinkMorph: Emergent Properties in Multimodal Chain-of-Thought
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ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning Gu et al.: https://
arxiv.org/abs/2510.27492 #ArtificialIntelligence #DeepLearning #MachineLearning -
Multi-Agent Neural Cellular Automata Develop Complex Emergent Behaviors
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Excited to release our new work: Petri Dish Neural Cellular Automatahttps://t.co/4mWwFOnbYl
— hardmaru (@hardmaru) 5 novembre 2025
We investigate how multi-agent NCAs can develop into artificial life 🦠 exhibiting complex, emergent behaviors like cyclic dynamics, territorial defense, and spontaneous cooperation! https://t.co/Wjp6KkurSQExcited to release our new work: Petri Dish Neural Cellular Automata https://
pub.sakana.ai/pdnca We investigate how multi-agent NCAs can develop into artificial life exhibiting complex, emergent behaviors like cyclic dynamics, territorial defense, and spontaneous cooperation! -
DataRater: Automatic Continuous Learning for Model Training Examples
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DataRater: can you automatically+continuously learn which examples will help the model the most? Fun working on this w/ awesome set of co-authors: @luisa_zintgraf
, @dancalian
, @greg_far
, @iurii_kemae, @matteohessel
, @shar_jeremy
, @junh_oh
, András György, Tom Schaul, @hado
, &