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Latest articles, page 87

  1. Rejection Sampling Fine-Tuning: How Filtering Model Outputs by Reward Replaced RLHF Complexity

    Rejection Sampling Fine-Tuning: How Filtering Model Outputs by Reward Replaced RLHF Complexity Rejection sampling fine-tuning (RAFT) has emerged as the practical workhorse of LLM alignment. While PPO-based RLHF dominated early literature, production systems from Llama 2 to DeepSeek-R1 rely on a simpler loop: generate multiple completions per prompt, score them with a reward model, keep the best, and fine-tune on the filtered data. The technique converts the reinforcement learning problem into s

    Education
    4 min read

  2. OKX and Goldman Sachs Cut Off Hong Kong Staff From Anthropic's Claude AI

    Crypto exchange OKX has restricted access to Anthropic's Claude model for employees based in Hong Kong or traveling through China after its corporate account was briefly suspended, according to Bloomberg. The restriction comes despite OKX making AI proficiency part of employee performance evaluations and encouraging workers to integrate AI tools into daily workflows. OKX stopped providing Claude access to staff in the region in early August following the suspension, Bloomberg reported. Internal

    Policy
    2 min read

  3. Vector Indexing in Production: HNSW vs. DiskANN vs. IVF-PQ Architecture, Memory Footprint, and Search Economics

    Vector Indexing in Production: HNSW vs. DiskANN vs. IVF-PQ Architecture, Memory Footprint, and Search Economics Scaling vector search beyond prototype deployments exposes a fundamental tension across three competing constraints: retrieval recall, query latency, and memory footprint. In high-dimensional representation spaces, exact k-nearest neighbor search via brute-force flat scans requires $O(N \cdot d)$ floating-point operations per query. For a corpus of 100 million 1536-dimensional FP32 em

    AI
    7 min read

  4. Rotational Quantization: How Orthogonal Transforms and Hadamard Incoherence Enable Outlier-Free Low-Bit Inference

    Large language model serving is governed by two physical bottlenecks: memory bandwidth during autoregressive decoding and compute throughput during prompt prefill. While weight-only post-training quantization (such as GPTQ or AWQ) reduces parameter footprint to 4 bits, it leaves activations in 16-bit floating-point formats. As a result, inference engines cannot utilize high-throughput INT4 tensor cores, and the key-value (KV) cache continues to consume massive memory pools. Attempting to quanti

    AI
    8 min read

  5. Anthropic Eyes $10B+ Credit Line Ahead of Planned Public Listing

    Anthropic is working to expand its revolving credit facility beyond an initial $10 billion target as it prepares for a planned initial public offering, according to reporting from Bloomberg. Wall Street investment banks are actively competing for lending allocations in the facility to improve their positioning for underwriting mandates on the eventual share sale. Under the framework currently under discussion, Anthropic has asked lead banks to commit approximately $1.25 billion each. Secondary

    Anthropic
    1 min read

  6. Multi-LoRA Serving in Production: Architecture, Dynamic Adapter Swapping, and GPU Memory Management

    Deploying hundreds or thousands of fine-tuned language models across enterprise workflows presents a fundamental infrastructure dilemma. While parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA) reduce training compute by freezing base model weights and training compact low-rank matrices, naive deployment strategies fail at scale. Merging adapter weights directly into the base model creates a distinct 7B to 70B parameter checkpoint for every specialized task, requiring

    AI
    8 min read

  7. Weight-Decomposed Low-Rank Adaptation (DoRA): How Decoupling Magnitude and Direction Closes the LoRA Gap

    Weight-Decomposed Low-Rank Adaptation (DoRA): How Decoupling Magnitude and Direction Closes the LoRA Gap Parameter-efficient fine-tuning (PEFT) has become the standard operational paradigm for adapting large language models to domain-specific downstream tasks. Among existing PEFT methodologies, Low-Rank Adaptation (LoRA) remains the default implementation across industry and academia due to its minimal parameter footprint and zero inference overhead. However, empirical studies consistently reve

    AI
    8 min read

  8. Attention with Linear Biases (ALiBi): How Static Positional Slopes Enable Zero-Shot Context Extrapolation

    Large language models process sequences by transforming discrete tokens into continuous vector representations. Standard dot-product self-attention is permutation-invariant: without explicit positional information, the attention operation treats a sequence as an unordered bag of tokens. Early transformer architectures addressed this limitation using Absolute Positional Embeddings (APE), either through fixed sinusoidal functions or learned lookup tables added directly to token embeddings. While

    AI
    6 min read

  9. Vals AI Raises $40M Series A at $400M Valuation Led by a16z to Build Real-World AI Benchmarks

    San Francisco evaluation startup Vals AI announced a $40 million Series A funding round at a $400 million post-money valuation, led by Andreessen Horowitz. The round included participation from existing seed backers 8VC, Pear VC, and Bloomberg Beta, alongside new institutional investors HRT Ventures and Next Ladder Ventures. The financing brings total capital raised by the company to $45 million, following a $5 million seed round. Founded by Stanford computer science graduates Rayan Krishnan a

    AI
    2 min read

  10. Small Language Models in Production: Task Specialization, Serving Economics, and the Frontier Offloading Pattern

    The default architecture for first-generation enterprise AI agents routed every prompt, tool selection, and intermediate evaluation step to a single frontier large language model. While this monolithic approach simplified initial orchestration, it introduced severe latency bottlenecks and unsustainable inference unit economics in high-throughput production environments. In production agentic loops, between 40% and 70% of model invocations are narrow, highly structured operations: classifying in

    AI
    6 min read