Speculative Decoding in vLLM: Draft, Verify, and Cut Latency Without Losing the Distribution
Large language model inference has an awkward performance profile: the GPU does enormous math, then waits. Every token requires a
Tag: Machine Learning
Harness Scaling: How a State Machine Runtime Pushed Agents to 95.3% on Terminal-Bench
The default answer to “make the agent better” is a better model. But a growing pile of evidence says that
Beyond Math and Code: How SpyRL Turns Party Games Into Verifiable Training Signals for LLMs
Reinforcement Learning with Verifiable Rewards (RLVR) has become the engine behind modern reasoning models. The recipe is straightforward: let a
Metis: The First Memory Foundation Model That Learns to Remember
AI agents have gotten remarkably good at reasoning, perceiving, and acting. But ask one to remember what you told it
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Xiaomi-Robotics-1: When Scaling Laws Finally Arrive in Robotics
Robotics has a data problem. While language and vision models have ridden scaling laws to ever-higher capabilities, robot learning has
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Inkling: Thinking Machines Lab’s 975B Open-Weights Multimodal Model
The open-weights LLM landscape just gained a significant new entrant. Inkling, released on July 15 by Thinking Machines Lab, is
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Training-Inference Mismatch: Why Your LLM Reinforcement Learning Is Optimizing the Wrong Policy
Reinforcement learning has become the defining ingredient of modern LLM post-training. GRPO, PPO, and their variants drive the reasoning capabilities
Program-as-Weights: Compiling Natural Language Into Local Neural Programs
There’s a class of programming tasks that resists clean implementation: deciding whether a log line is “important,” repairing malformed JSON
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Why Verification Is Harder Than Generation for AI Coding Agents
There’s a classical intuition in computer science that verifying a solution is easier than finding one. For NP-complete problems, this
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LoopCoder-v2: Why Two Loops Beat Four in Test-Time Compute Scaling
The dominant scaling narrative in large language models has been straightforward: more parameters, more data, more compute. But there’s a
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