🔥 𝐍𝐞𝐰 𝐛𝐥𝐨𝐠: 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐋𝐨𝐨𝐩𝐞𝐝 𝐌𝐨𝐝𝐞𝐥𝐬 𝐃𝐨𝐧𝐞 𝐑𝐢𝐠𝐡𝐭 — 𝐏𝐚𝐫𝐭 𝐈 Looped models reuse the same weights across depth, promising a better compute–parameter trade-off, especially for reasoning. 𝐁𝐮𝐭 𝟏) 𝐝𝐨 𝐭𝐡𝐞 𝐠𝐚𝐢𝐧𝐬 𝐬𝐮𝐫𝐯𝐢𝐯𝐞 𝐰𝐡𝐞𝐧 𝐛𝐨𝐭𝐡 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐅𝐋𝐎𝐏𝐬 𝐚𝐫𝐞 𝐦𝐚𝐭𝐜𝐡𝐞𝐝? 𝟐) 𝐀𝐧𝐝 𝐰𝐡𝐢𝐜𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐜𝐡𝐨𝐢𝐜𝐞𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐭𝐭𝐞𝐫? We run 𝐚𝐩𝐩𝐥𝐞𝐬-𝐭𝐨-𝐚𝐩𝐩𝐥𝐞𝐬 ablations spanning Ouro to Huginn. Huginn performs better overall, with the largest gains coming from the loop-in-the-middle (sandwich) design and input injection, though they provide different benefits. Trained on 𝟓𝟎𝟎𝐁 tokens, an 𝟖𝐁-𝐀𝟎.𝟖𝐁 Huginn MoE approaches or surpasses a 𝟑𝟐𝐁-𝐀𝟑.𝟐𝐁 feedforward MoE on several reasoning benchmarks, including GSM8K (83.6% vs. 80.8%), while using 𝟕𝟓% 𝐟𝐞𝐰𝐞𝐫 resident parameters under 𝐦𝐚𝐭𝐜𝐡𝐞𝐝 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 FLOPs. More details and the blog link in the thread ↓