Disentangling Harness Updating and Benefit in LLM Agents
💡Discover why bigger models aren't always better at self-evolution and how to optimize your agent's harness strategy.
⚡ 30-Second TL;DR
What Changed
Harness-updating capability is consistent across different model tiers.
Why It Matters
The findings suggest that developers should focus on improving agent reliability and instruction-following rather than just scaling model size for self-evolving systems. This shifts the strategy for building autonomous agents toward better harness invocation mechanisms.
What To Do Next
Evaluate your agent's performance by testing if it can faithfully follow updated system prompts or tool definitions before scaling to larger models.
Key Points
- •Harness-updating capability is consistent across different model tiers.
- •Harness-benefit is non-monotonic, with mid-tier models outperforming both weak and strong models.
- •Weak-tier models struggle to activate and faithfully follow updated harness artifacts.
- •Investment should prioritize improving agent task-solving and instruction-following over evolver capability.
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Original source: ArXiv AI ↗
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