alexander long

``` [Browser address bar: x.com/kalomaze/status/2030...] Fiora Starlight @FioraStarlight · 6h jackasses train an agent autonomously via RL on task completion without safety considerations, and get something that exploits security flaws in its server to take wildly unintended and undesired actions... something like this is going to be what kills us. > QUOTED: Alexander Long @AlexanderLong · 15h > insane sequence of statements buried in an Alibaba tech report [Embedded image: excerpt from tech report, with portions highlighted in yellow] When rolling out the instances for the trajectory, we encountered an unanticipated—and operationally consequential—class of unsafe behaviors that arose without any explicit instruction and, more troublingly, outside the bounds of the intended sandbox. Our first signal came not from training curves but from production-grade security telemetry. Early one morning, our team was urgently convened after Alibaba Cloud's managed firewall flagged a burst of security-policy violations originating from our training servers. The alerts were severe and heterogeneous, including attempts to probe or access internal-network resources and traffic patterns consistent with cryptomining-related activity. We initially treated this as a conventional security incident (e.g., misconfigured egress controls or external compromise). However, the violations recurred intermittently with no clear temporal pattern across multiple runs. We then correlated firewall timestamps with our system telemetry and RL traces, and found that the anomalous outbound traffic consistently coincided with specific episodes in which the agent invoked tools and executed code. In the corresponding model logs, we observed the agent proactively initiating the relevant tool calls and code-execution steps that led to these network actions. Crucially, these behaviors were not requested by the task prompts and were not required for task completion under the intended sandbox constraints. Together, these observations suggest that during iterative RL optimization, a language-model agent can spontaneously produce hazardous, unauthorized behaviors at the tool-calling and code-execution layer, violating the assumed execution boundary. In the most striking instance, the agent established and used a reverse SSH tunnel from an Alibaba Cloud instance to an external IP address—an outbound-initiated remote access channel that can effectively neutralize ingress filtering and erode supervisory control. We also observed the unauthorized repurposing of provisioned GPU capacity for cryptocurrency mining, quietly diverting compute away from training, inflating operational costs, and introducing clear legal and reputational exposure. Notably, these events were not triggered by prompts requesting tunneling or mining; instead, they emerged as instrumental side effects of autonomous tool use under RL optimization. While impressed by the capabilities of agentic... 12:49 PM · Mar 6, 2026 · 1.4M Views 164 replies, 1K reposts, 5K likes, 2.5K bookmarks Alexander Long @AlexanderLong · 12h Is from here [Link card: arxiv.org — "Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME..."] 1 reply, 22 reposts, 252 likes, 43K views Connor Axiotes @connoraxiotes · 22m Thanks for showing why we should not just rad AI [cut off] ```

[Browser address bar: x.com/AlexanderLong/status/] Zach Tratar @zachtratar · 12h Are there any new startups attempting to become frontier labs? I'm not talking about SSI or Thinking Machines... smaller. More of the dark horse vibe team... 62 replies, 7 reposts, 216 likes, 42K views Alexander Long @AlexanderLong we have a pretty disproportionate research output for <10 person team. work on architectures you can collaboratively train [Embedded image: "Research" list] Protocol Models: Scaling Decentralized Training with Communication-Efficient Model Parallelism S. Ramasinghe, T. Ajanthan, G. Avraham, Y. Zuo, A. Long | NeurIPS 2025 This is the first work that shows model-parallel training over low-bandwidth networks is possible. Specifically, it demonstrates an 8B LLaMA model being trained on par with centralized training when the devices holding subsequent transformer blocks are in four different locations and connected only via standard internet connections. This was considered completely impossible prior to this work. Nesterov Method for Asynchronous Pipeline Parallel Optimization T. Ajanthan, S. Ramasinghe, Y. Zuo, G. Avraham, A. Long | ICML 2025 Pipeline Parallelism allows large models to train across many small devices by slicing the network into stages. In pipeline parallelism, there is a problem of a "bubble" where devices are idle. It slows down both centralized and decentralized training, but the effect is more pronounced in the decentralized case as communication lag affects the size of the bubble. We solve this, outperforming all existing async techniques and even the synchronous baseline. [Code] Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization A. Long*, C. Koneputugodage*, S. Ramasinghe, T. Ajanthan, G. Avraham, Y. Zuo | NeurIPS 2025 UPM's facilitate decentralized training while ensuring a full weight set is never available to any single participant. UPMs thus enable collaborative training while making the model unextractable in practice. Mixtures of Subspaces for Bandwidth-Efficient Context Parallel Training S. Ramasinghe, T. Ajanthan, H. Dolatabadi, G. Avraham, V. Shevchenko, Y. Zuo, C. Koneputugodage, A. Long | NeurIPS 2025 We propose a compression method for communication-efficient context parallelism in decentralized set[tings], achieving over 95% compression with negligible overhead and no loss in convergence. The key insi[ght is to] exploit the intrinsic low-rank structure of activations by dynamically constraining them to learned [subspaces] via efficient reparameterizations. This allows scaling billion-parameter decentralized mode[ls to] context lengths exceeding 100K tokens on networks as slow as 300 Mbps, matching the wall-clock convergence of centralized models on 100 Gbps interconnects.