Killing Coding Agent Slop With Adversarial Self-Play
Summary
The article introduces adversarial self-play as a technique to eliminate low-quality outputs ('slop') from coding agents, improving their reliability and performance.
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How to Avoid AI Code Slop
This newsletter article discusses the challenge of AI-generated code outpacing human code review, leading to 'AI code slop', and offers strategies to balance speed and quality.
@kentcdodds: Engineering away "slop" is one of the most fun things I do! I don't love the term "slop" because it frames it like this…
Kent C. Dodds argues that preventing AI 'slop' is a straightforward three-step engineering process — high-quality agent PRs, solid PR review, and feature flags with disaster recovery — noting these practices long predate AI agents.
@kentcdodds: If your agents are making slop, don't give up or fight the agent with cleanup. Work on the primitives the agents have t…
Kent C. Dodds advises improving the primitives used by AI agents to reduce low-quality outputs instead of relying on cleanup, illustrating the point with a linked thread.
Self-play helped AI achieve superhuman performance in Go, so why hasn’t it done the same for LLMs? Researchers have found a solution.
Researchers introduce Self-Guided Self-Play (SGS), a self-play algorithm for LLMs that prevents reward hacking by using a Guide role to score synthetic problems. Applied to theorem proving in Lean4, SGS surpasses RL baselines and allows a 7B model to outperform a 671B model.
@rohanpaul_ai: Brilliant new paper from Meta, CMU and other labs. Shows that coding agents improve faster by manufacturing their own s…
A new paper from Meta, CMU, and other labs presents Self-play SWE-RL, a method where coding agents train themselves by manufacturing and fixing bugs in real codebases, achieving significant gains on SWE-bench benchmarks without relying on human-written tasks.