February 2026

Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning

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Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics. Existing methods typically address dynamics mismatch either globally over the state space or via pointwise data filtering; these approaches can miss localized cross-domain similarities or incur high computational cost. We propose Localized Dynamics-Aware Domain Adaptation (LoDADA), which exploits localized dynamics mismatch to better reuse source data. LoDADA clusters transitions from source […]

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go-size-analyzer

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go-size-analyzer The Go ecosystem is really good at tooling. I just learned about this tool for analyzing the size of Go binaries using a pleasing treemap view of their bundled dependencies. You can install and run the tool locally, but it’s also compiled to WebAssembly and hosted at gsa.zxilly.dev – which means you can open compiled Go binaries and analyze them directly in your browser. I tried it with a 8.1MB macOS compiled copy of my Go Showboat […]

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Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise

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Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given $k$ data sources, the goal is to output a classifier for each source by exploiting shared structure to reduce sample complexity. We focus on the bounded label noise setting to determine whether the fast $1/ε$ rates achievable in single-task learning extend to this regime with minimal dependence on $k$. Surprisingly, we show that this is not the case. We demonstrate […]

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Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

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The rapid advancement of artificial intelligence (AI) has created unprecedented demand for specialized models capable of complex reasoning tasks, particularly in competitive programming where models must generate functional code through algorithmic reasoning rather than pattern memorization. Reinforcement learning (RL) enables models to learn through trial and error by receiving rewards based on actual code execution, making it particularly well-suited for developing genuine problem-solving capabilities in algorithmic domains. However, implementing distributed RL training for code generation presents significant infrastructure […]

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Generate structured output from LLMs with Dottxt Outlines in AWS

Generate structured output from LLMs with Dottxt Outlines in AWS

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This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that are predefined, validated, and often strictly entered. This can include the schema for the output, or ways specific fields in the output should be mapped. Structured outputs are essential for applications that require consistency, validation, and seamless integration with downstream systems. For example, banking loan approval systems must generate JSON outputs with […]

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Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

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Organizations across in Thailand, Malaysia, Singapore, Indonesia, and Taiwan can now access Anthropic Claude Opus 4.6, Sonnet 4.6, and Claude Haiku 4.5 through Global cross-Region inference (CRIS) on Amazon Bedrock—delivering foundation models through a globally distributed inference architecture designed for scale. Global CRIS offers three key advantages: higher quotas, cost efficiency, and intelligent request routing to inference capacity across AWS commercial Regions for enabling AI use-cases like chatbots, autonomous coding agents, and financial analysis systems for customers. In […]

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Matching Multiple Experts: On the Exploitability of Multi-Agent Imitation Learning

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Multi-agent imitation learning (MA-IL) aims to learn optimal policies from expert demonstrations of interactions in multi-agent interactive domains. Despite existing guarantees on the performance of the resulting learned policies, characterizations of how far the learned polices are from a Nash equilibrium are missing for offline MA-IL. In this paper, we demonstrate impossibility and hardness results of learning low-exploitable policies in general $n$-player Markov Games. We do so by providing examples where even exact measure matching fails, and demonstrating […]

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Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

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We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. This launch supports organizations in the Middle East to access Anthropic’s latest Claude models on Amazon Bedrock while benefiting from global, highly available inference routing across the AWS network. With global cross-Region inference, you can scale inference workloads seamlessly, improve resiliency, […]

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International AI Safety Report 2026

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The International AI Safety Report 2026 synthesises the current scientific evidence on the capabilities, emerging risks, and safety of general-purpose AI systems. The report series was mandated by the nations attending the AI Safety Summit in Bletchley, UK. 29 nations, the UN, the OECD, and the EU each nominated a representative to the report’s Expert Advisory Panel. Over 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the Report’s Chair, these independent experts collectively had full […]

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Lamborghini cancels electric Lanzador as supercar buyers reject EVs

Lamborghini cancels electric Lanzador as supercar buyers reject EVs

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For the last few years, Lamborghini has been in a quandary: What to do about an electric vehicle? Among the supercar brands, Lamborghini has always stood out as favoring drama over lap times. And while electric motors and their instant torque can make a car accelerate very quickly indeed, other than the G-forces, it happens with such little fuss. Working out how to imbue an EV with enough “wow” factor to wear the famous bull badge has proved […]

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