Over-Defense in Coding Agents Begins at Silent Forks
Language models behind coding agents are post-trained with outcome rewards, and recent methods concentrate credit on the turns that decide the outcome.
ProofPaper ↗
Key points
- We find that defensive actions beyond what a task requires escape both.
- They begin at silent forks, the turns where every next action leads to the same outcome.
- This motivates a rethink of where learning signals belong, namely where behavior diverges, which a behavioral checker can locate and the outcome cannot.
- Our method, ForkDPO, contrasts a proportionate and a disproportionate action at the same prefix, at silent forks on both sides of the required behavior.
Sources (1)
- [1]Over-Defense in Coding Agents Begins at Silent ForksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 01:56 AM
Language models behind coding agents are post-trained with outcome rewards, and recent methods concentrate credit on the turns that decide the outcome.
We find that defensive actions beyond what a task requires escape both.
Extractive summary: sentences quoted from the sources.
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