How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed.
Happy to answer questions about the implementation or the research!
How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed.
Happy to answer questions about the implementation or the research!
vebgen•16h ago
A few questions about your implementation:
1. How do you handle the balance between delta updates and full context rewrites when the playbook grows large? We've found that keeping detailed history helps with debugging but can bloat context quickly.
2. The Generator/Reflector/Curator separation is elegant. Did you implement these as separate LLM calls or different prompting strategies on the same model? We use a similar dual-agent pattern (planner + executor) and the coordination overhead is non-trivial.
3. Most interesting part: "natural execution feedback without labeled supervision." How do you define success/failure signals for the Reflector in ambiguous cases? For code generation, it's easy (tests pass/fail), but for other domains it seems trickier.
The +10.6% improvement on agent tasks is impressive - definitely checking out the paper. The brevity bias problem you mention is real - we've noticed agents dropping important context details when trying to "summarize efficiently."