𝗛𝗮𝗿𝗻𝗲𝗌𝗦𝗲𝗮𝗿𝗰𝗵: 𝗔 𝗡𝗲𝘄 𝗪𝗮𝘆 𝗧𝗼 𝗦𝗲𝗮𝗿𝗰𝗵 You want to improve your search results. A new approach is here. It's called Harness-1. This is a search agent that uses a different method to manage its memory.
Here's how it works:
- The agent keeps its memory outside of its main context.
- It only looks at a small part of its memory at a time.
- This helps the agent to keep searching without running out of space.
The old way of doing things was to keep adding to the agent's memory. This made it harder for the agent to find what it was looking for. Harness-1 is different. It keeps the important information in a separate space. This makes it easier for the agent to find what it needs.
Harness-1 is trained using reinforcement learning. This means it learns by trying different things and seeing what works best. It's a more efficient way of searching. The agent can keep looking for information without getting overwhelmed.
The results are promising. Harness-1 can find more relevant information than other search agents. It's a new way of thinking about search. You can learn more about it and join the conversation. Source: https://dev.to/pueding/harness-1-state-externalizing-search-harness-2c9b Optional learning community: https://t.me/GyaanSetuAi