@cartathomas.bsky.social
𝐐𝟒: 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐆𝐨𝐚𝐥 𝐒𝐩𝐚𝐜𝐞𝐬
We replaced the 𝐞𝐧𝐭𝐢𝐫𝐞 𝐠𝐨𝐚𝐥 𝐬𝐩𝐚𝐜𝐞 with unseen goals from the same categories. 🧭MAGELLAN generalized LP and retained exceptional performance—matching baselines that rely on human expertise! 🚀✨
We replaced the 𝐞𝐧𝐭𝐢𝐫𝐞 𝐠𝐨𝐚𝐥 𝐬𝐩𝐚𝐜𝐞 with unseen goals from the same categories. 🧭MAGELLAN generalized LP and retained exceptional performance—matching baselines that rely on human expertise! 🚀✨
March 24, 2025 at 3:09 PM
𝐐𝟒: 𝐀𝐝𝐚𝐩𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐆𝐨𝐚𝐥 𝐒𝐩𝐚𝐜𝐞𝐬
We replaced the 𝐞𝐧𝐭𝐢𝐫𝐞 𝐠𝐨𝐚𝐥 𝐬𝐩𝐚𝐜𝐞 with unseen goals from the same categories. 🧭MAGELLAN generalized LP and retained exceptional performance—matching baselines that rely on human expertise! 🚀✨
We replaced the 𝐞𝐧𝐭𝐢𝐫𝐞 𝐠𝐨𝐚𝐥 𝐬𝐩𝐚𝐜𝐞 with unseen goals from the same categories. 🧭MAGELLAN generalized LP and retained exceptional performance—matching baselines that rely on human expertise! 🚀✨
𝐐𝟑: 𝐆𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
At the end of training, 🧭MAGELLAN has 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞, consistently 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 success probability 𝐟𝐨𝐫 𝐮𝐧𝐬𝐞𝐞𝐧 𝐠𝐨𝐚𝐥𝐬, a key step toward scalable open-ended learning!
At the end of training, 🧭MAGELLAN has 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞, consistently 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 success probability 𝐟𝐨𝐫 𝐮𝐧𝐬𝐞𝐞𝐧 𝐠𝐨𝐚𝐥𝐬, a key step toward scalable open-ended learning!
March 24, 2025 at 3:09 PM
𝐐𝟑: 𝐆𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧
At the end of training, 🧭MAGELLAN has 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞, consistently 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 success probability 𝐟𝐨𝐫 𝐮𝐧𝐬𝐞𝐞𝐧 𝐠𝐨𝐚𝐥𝐬, a key step toward scalable open-ended learning!
At the end of training, 🧭MAGELLAN has 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥 𝐞𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐬𝐩𝐚𝐜𝐞, consistently 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐧𝐠 success probability 𝐟𝐨𝐫 𝐮𝐧𝐬𝐞𝐞𝐧 𝐠𝐨𝐚𝐥𝐬, a key step toward scalable open-ended learning!
𝐐𝟐: 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
🧭MAGELLAN autonomously discovers goal families (✊🌿🐮🦁) across 𝟐𝟓𝐤 𝐠𝐨𝐚𝐥𝐬, performing on par with expert knowledge-augmented baselines—but 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐩𝐫𝐞𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐠𝐨𝐚𝐥 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬! 🚀
🧭MAGELLAN autonomously discovers goal families (✊🌿🐮🦁) across 𝟐𝟓𝐤 𝐠𝐨𝐚𝐥𝐬, performing on par with expert knowledge-augmented baselines—but 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐩𝐫𝐞𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐠𝐨𝐚𝐥 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬! 🚀
March 24, 2025 at 3:09 PM
𝐐𝟐: 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
🧭MAGELLAN autonomously discovers goal families (✊🌿🐮🦁) across 𝟐𝟓𝐤 𝐠𝐨𝐚𝐥𝐬, performing on par with expert knowledge-augmented baselines—but 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐩𝐫𝐞𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐠𝐨𝐚𝐥 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬! 🚀
🧭MAGELLAN autonomously discovers goal families (✊🌿🐮🦁) across 𝟐𝟓𝐤 𝐠𝐨𝐚𝐥𝐬, performing on par with expert knowledge-augmented baselines—but 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐩𝐫𝐞𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐠𝐨𝐚𝐥 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬! 🚀
🎯 𝐐𝟏: 𝐂𝐨𝐦𝐩𝐞𝐭𝐞𝐧𝐜𝐞 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧
🧭MAGELLAN matches expert baselines in estimating competence over tens of thousands of goals but with 𝐦𝐢𝐧𝐢𝐦𝐚𝐥 𝐜𝐨𝐬𝐭 & 𝐞𝐫𝐫𝐨𝐫! Unlike other methods, it efficiently tracks competence transfer across large goal spaces
🧭MAGELLAN matches expert baselines in estimating competence over tens of thousands of goals but with 𝐦𝐢𝐧𝐢𝐦𝐚𝐥 𝐜𝐨𝐬𝐭 & 𝐞𝐫𝐫𝐨𝐫! Unlike other methods, it efficiently tracks competence transfer across large goal spaces
March 24, 2025 at 3:09 PM
🎯 𝐐𝟏: 𝐂𝐨𝐦𝐩𝐞𝐭𝐞𝐧𝐜𝐞 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧
🧭MAGELLAN matches expert baselines in estimating competence over tens of thousands of goals but with 𝐦𝐢𝐧𝐢𝐦𝐚𝐥 𝐜𝐨𝐬𝐭 & 𝐞𝐫𝐫𝐨𝐫! Unlike other methods, it efficiently tracks competence transfer across large goal spaces
🧭MAGELLAN matches expert baselines in estimating competence over tens of thousands of goals but with 𝐦𝐢𝐧𝐢𝐦𝐚𝐥 𝐜𝐨𝐬𝐭 & 𝐞𝐫𝐫𝐨𝐫! Unlike other methods, it efficiently tracks competence transfer across large goal spaces
Our LLM agent uses 🧭MAGELLAN to estimate past & current competence, computing 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 (𝐀𝐋𝐏) for each goal. The agent then selects goals that maximize ALP, learning efficiently via online RL. 🚀 #ReinforcementLearning #LLM
March 24, 2025 at 3:09 PM
Our LLM agent uses 🧭MAGELLAN to estimate past & current competence, computing 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 (𝐀𝐋𝐏) for each goal. The agent then selects goals that maximize ALP, learning efficiently via online RL. 🚀 #ReinforcementLearning #LLM