Building Agent Bubbles: A Practical Guide to Personal Online RLHF with Qwen
cephaloform · x · 2026-08-26
The author shares the implementation of "Agent Bubbles," a local assistant trained using an online reinforcement learning loop.
- Architecture: By day, the Qwen-based agent executes tasks; by night, it acts as a reward model. A separate "value model" predicts rewards based on embedded memory to compute advantages.
- Training: Uses the OAPL algorithm with a batch size of 64 and incorporates an imitation loss (ECHO objective) to build an implicit world model.
- Refinement: A secondary pass on 30% of rollouts evaluates mutual understanding to calibrate rewards.
This post offers a technical workflow for developers interested in training their own agents.
Related event: Developer Builds Local Agent Bubbles: Works by Day, Self-Improves by Night(2 posts)→
More from coding & agent
- How do teams collaborate with AI coding agents in real time? Tried everything obvious — BriefHillEncounter · 2026-08-26
- Runway launches MCP: generate video from Claude, ChatGPT, and Cursor — tlakomy · 2026-08-26
- CyberFactory Framework Boosts Cybersecurity Agents with Real Vulnerability Data — IQuestLab · 2026-08-26
- Fixing ComfyUI Minimax H3 upscaler's model path discovery for external directories — Slight-Living-8098 · 2026-08-26
- Dev releases lightweight coding agent in C with hot-reloadable Lua plugins — horrificrabbit · 2026-08-26
- Developer builds local agent "Secret Agent Bubbles": Qwen works by day, self-trains by RL at night — cephaloform · 2026-08-26