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In this video, we break down Poolside’s Laguna S2.1, an open-weights 118B MoE coding model (8B active per token) with a 1M-token context window, and why it performs above its size on agentic coding benchmarks like Terminal Bench 2.1. I cover how it was trained with reinforcement learning in FP8 on ~4,000 NVIDIA H200s in under nine weeks, plus how they tackled reward hacking on SWE-bench using an external LLM judge, prompt amendments, and network-blocked sandboxes.
Thanks to @NVIDIADeveloper for DGX Spark.
Laguna: https://poolside.ai/blog/introducing-laguna-s-2-1
DGX Spark: https://nvda.ws/3XIkwsh
Try it out: https://chat.poolside.ai/
Pool Agent Harness: https://poolside.ai/get-started
vLLM Serving: https://github.com/MiaAI-Lab/Laguna-S-2.1-DGX-Spark-RTX-6000-PRO
DSpark video: https://youtu.be/eFgknPFK-g0
MoE Quantization paper: https://arxiv.org/pdf/2606.00206
My voice to text App: whryte.com
Website: https://engineerprompt.ai/
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00:00 Laguna S2.1 Overview
01:17 Benchmarks and Harness
02:13 Reinforcement Learning
03:34 Reward Hacking Fixes
05:16 Running on DGX Spark
06:55 NVFP4 Quantization
08:03 Speculative Decoding Speed
10:09 Pool Harness Demo
12:07 Verbose Reasoning Loops Read More Prompt Engineering
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