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Ant's ASystem team launches AReno local post-training toolkit
On August 14, 2026, Ant's ASystem team launched the AReno local large-model post-training toolkit. On the same day, Ant Ling released a practice article describing how AReno was used on DGX Spark to perform Agentic RL on Ling-3.0-tiny, using tic-tac-toe as the minimal validation task and completing a four-step closed loop of baseline evaluation, reward definition, post-training, and re-testing in the same environment. The experiment used GSPO to train for 400 steps; rollout/rewards_mean rose from about -0.5 to around 0.4, response_len dropped to about 850 tokens, and in the re-test, tool calls and move selection were more stable. Ling-3.0-tiny is a hybrid reasoning model with 7.9B total parameters and 1.3B activated parameters, and BF16, FP8, and INT4 versions have been open-sourced; AReno provides a Docker reproduction tutorial. The toolkit has now been made public together with the above practice.
System team launched the AReno local large-model post-training toolkit. On the same day, Ant Ling released a practice article describing how AReno was used on DGX Spark to perform Agentic RL on Ling-3.0-tiny, using tic-tac-toe as the minimal validation task and completing a four-step closed loop of baseline evaluation, reward definition, post-training, and re-testing in the same environment. The experiment used GSPO to train for 400 steps; rollout/rewards mean rose from about -0.5 to around 0.4,
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LMSYS explains in detail how HiCache adapts to multi-parallelism and distributed inference
The LMSYS blog introduces how HiCache is implemented in context parallelism, pipeline parallelism, speculative decoding, unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by 40.48 percentage points, the host memory serving token volume dropped by 81.2%, and the average prefill queueing time decreased by 56.2%.
The LMSYS blog introduces how HiCache is implemented in context parallelism, pipeline parallelism, speculative decoding, unified KV layout, and P/D disaggregation scenarios, with the core being to keep cache decisions consistent across ranks. The iFlytek Spark MaaS team used HiCache plus external storage to serve GLM-5.1/5.2 CodingPlan workloads, raising the cache hit rate from about 30% to 95% and reducing median TTFT to under 3 seconds. In CP sharding tests, the GPU token hit rate increased by
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Building Efficient Agents with Knowledge Distillation on Amazon SageMaker AI
An AWS official blog post explains how to fine-tune a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): using GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentially on par with the teacher model and mid-sized frontier models; when the teacher's reasoning process was removed and only tool calls were learned, the average success rate dropped to 0.415.
An AWS official blog post explains how to fine-tune a shopping agent on Amazon SageMaker AI using sequence-level knowledge distillation (SeqKD): using GLM-5.3 on Amazon Bedrock as the teacher model to generate trajectories, using the ShoppingBench scorer to retain only trajectories that solve the task, and then performing serverless SFT on the Qwen3.5-4B student model. On 324 held-out test questions, the distilled 4B student's average absolute success rate increased from 0.297 to 0.541, essentia
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Reinforcement Learning Terminology for LLM Readers: How a Single Score Is Distributed to Each Token
The article uses DeepSeek-R1-Zero's pass@1 on AIME 2024 rising from 15.6% to 71.0% as an example to show that a rule-based judge gives only a single score for the entire response, whereas training needs to adjust the probability of each token. The author uses the sampling example "Is OpenAI open?" to explain in turn policy, state, action, reward, return, value, Q, advantage, baseline, TD error, GAE, clipping, and KL, and points out that PPO uses a critic to learn V, while GRPO uses the average score within a group to replace V.
The article uses DeepSeek-R1-Zero's pass@1 on AIME 2024 rising from 15.6% to 71.0% as an example to show that a rule-based judge gives only a single score for the entire response, whereas training needs to adjust the probability of each token. The author uses the sampling example "Is OpenAI open?" to explain in turn policy, state, action, reward, return, value, Q, advantage, baseline, TD error, GAE, clipping, and KL, and points out that PPO uses a critic to learn V, while GRPO uses the average s
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Pydantic compares code mode and tool calling across 5,276 runs
Michael Pfaffenberger of Pydantic used 5 small Pydantic AI applications and 8 models to conduct 5,276 scored runs, comparing code mode with direct tool calling. On long tasks, code mode reduced average cost by 58%, latency by 51%, and increased success rate by 16 percentage points; however, for short tasks with 5 items, 3 out of 5 tasks were instead 21% to 38% more expensive. The author argues that models are better at writing loops than at acting as the loop, and the gap widens as tasks get longer.
Michael Pfaffenberger of Pydantic used 5 small Pydantic AI applications and 8 models to conduct 5,276 scored runs, comparing code mode with direct tool calling. On long tasks, code mode reduced average cost by 58%, latency by 51%, and increased success rate by 16 percentage points; however, for short tasks with 5 items, 3 out of 5 tasks were instead 21% to 38% more expensive. The author argues that models are better at writing loops than at acting as the loop, and the gap widens as tasks get lon
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Perplexity Publishes LLM Gateway Best Practices Guide, Outlining Request Flow and Production Risks
On October 27, 2026, Perplexity published an LLM gateway best practices guide explaining how this middleware layer routes, authenticates, caches, and logs model calls, and distinguishing four categories: API gateways, LLM gateways, MCP gateways, and AI gateways. The guide breaks down the mechanism through a six-step request lifecycle, while warning that the gateway centralizes credentials from various providers, and cites the LiteLLM gateway breach disclosed by Microsoft and two high-severity vulnerabilities, CVE-2026-35029 and CVE-2026-59823, reported by Check Point (already fixed in LiteLLM 1.83.9). The article also lists postmortems of Cloudflare AI Gateway and OpenRouter outages, four deployment paths, and paid plans starting at about $50 per month.
On October 27, 2026, Perplexity published an LLM gateway best practices guide explaining how this middleware layer routes, authenticates, caches, and logs model calls, and distinguishing four categories: API gateways, LLM gateways, MCP gateways, and AI gateways. The guide breaks down the mechanism through a six-step request lifecycle, while warning that the gateway centralizes credentials from various providers, and cites the LiteLLM gateway breach disclosed by Microsoft and two high-severity vu
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The author built an "EQ plugin" with Claude Opus 5.5 and used UUMit to turn a local service into a team link
The author used Cursor together with Claude Opus 5.5 to build a zero-dependency local web tool called "EQ plugin," which rewrites emotionally charged drafts according to the four elements of Nonviolent Communication, and includes seven types of communication targets and three tone levels: gentle, firm, and direct. The tool runs on the local machine at 127.0.0.1:8787, and the UUMit client generates a fixed HTTPS link to distribute to colleagues, with no need to buy a server or file for registration, and permissions can be granted and revoked per person. The article also provides an 800-character limit per input, default rate limits of 10 times per minute and 200 times per day, as well as the source code and System Prompt for two GitHub repositories.
The author used Cursor together with Claude Opus 5.5 to build a zero-dependency local web tool called "EQ plugin," which rewrites emotionally charged drafts according to the four elements of Nonviolent Communication, and includes seven types of communication targets and three tone levels: gentle, firm, and direct. The tool runs on the local machine at 127.0.0.1:8787, and the UUMit client generates a fixed HTTPS link to distribute to colleagues, with no need to buy a server or file for registrati
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Google A2UI Team and CopilotKit Explain How to Build Generative UI Applications
The Google A2UI team and CopilotKit CEO Atai Barkai explain how to build generative UI applications using the A2UI open standard and CopilotKit. A2UI describes interfaces with declarative JSON, and the client only renders components from a trusted component catalog; the same response can be rendered to React, Angular, SwiftUI, Jetpack Compose, and Flutter. The article provides a code example for integrating CopilotKit with Google ADK, and explains the bidirectional flow, implemented via the AG-UI protocol, in which user actions are sent back and the agent regenerates the interface.
pplications using the A2UI open standard and CopilotKit. A2UI describes interfaces with declarative JSON, and the client only renders components from a trusted component catalog; the same response can be rendered to React, Angular, SwiftUI, Jetpack Compose, and Flutter. The article provides a code example for integrating CopilotKit with Google ADK, and explains the bidirectional flow, implemented via the AG-UI protocol, in which user actions are sent back and the agent regenerates the interface.
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Beijing News tests Qwen Go! and Lingguang 2.0: personal agent competition shifts toward autonomous task handling
Beijing News Shell Finance reporters tested Alibaba's Qwen Go! and Ant's Lingguang 2.0. The former set up a daily 8 a.m. medication reminder with a single sentence and checked it item by item, while the latter, after authorization on Alipay, sorted out the past six months of bills. The article also reviews the layouts of Meta Muse, OpenAI dots, as well as ByteDance and Tencent, and points out that cross-ecosystem permissions are currently the hardest wall to break down, with ByteDance's SAEP published rejection list reaching 155 apps.
Beijing News Shell Finance reporters tested Alibaba's Qwen Go! and Ant's Lingguang 2.0. The former set up a daily 8 a.m. medication reminder with a single sentence and checked it item by item, while the latter, after authorization on Alipay, sorted out the past six months of bills. The article also reviews the layouts of Meta Muse, OpenAI dots, as well as ByteDance and Tencent, and points out that cross-ecosystem permissions are currently the hardest wall to break down, with ByteDance's SAEP pub
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Browser Use Tutorial: How to Log In Daily and Download Invoices on Websites Without an API
Browser Use has published a tutorial introducing three approaches for automatically logging in and downloading invoices daily on supplier portals that lack an API: Playwright scripts, RPA tools such as UiPath or Power Automate, and browser agents with saved configurations. The article provides complete code for Playwright and Browser Use Cloud, covering reusing login sessions, filling in TOTP verification codes with 1Password, scheduling runs via GitHub Actions, and handling failure scenarios such as session expiration, 2FA blocks, page redesigns, and empty downloads. The author recommends first asking suppliers for exports, APIs, or emailed invoices, and reminds readers to first confirm whether the portal's terms permit automated access.
aches for automatically logging in and downloading invoices daily on supplier portals that lack an API: Playwright scripts, RPA tools such as UiPath or Power Automate, and browser agents with saved configurations. The article provides complete code for Playwright and Browser Use Cloud, covering reusing login sessions, filling in TOTP verification codes with 1Password, scheduling runs via GitHub Actions, and handling failure scenarios such as session expiration, 2FA blocks, page redesigns, and em
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How to Extract Structured Data from Websites with LLMs (2026)
Browser Use published a tutorial introducing a workflow for extracting structured data across multiple websites using a single JSON Schema: static pages are scraped to Markdown and then filled in by an LLM, while pages requiring login or clicks are handed off to a browser agent. The article provides a reliability checklist, including enabling structured output, validating with Pydantic or Zod, returning null for missing fields, preserving source_url and original-text citations, deduplicating by stable keys, respecting robots.txt, and spot-checking samples. It also compares Firecrawl, Crawl4AI, Browser Use, Stagehand, and a self-built Fetch+Markdown+LLM setup in terms of login and JS workflows, Schema support, open-source status, and billing.
multiple websites using a single JSON Schema: static pages are scraped to Markdown and then filled in by an LLM, while pages requiring login or clicks are handed off to a browser agent. The article provides a reliability checklist, including enabling structured output, validating with Pydantic or Zod, returning null for missing fields, preserving source url and original-text citations, deduplicating by stable keys, respecting robots.txt, and spot-checking samples. It also compares Firecrawl, Cr
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