Newer Claude models sometimes invent extra keys in tool call arguments, breaking validation in Pi's edit tool. The author suspects post-training for Claude Code's forgiving harness makes alternative schemas fail. This suggests closed RL training can degrade general tool-use reliability.
Background
- **Pi** is an open-source terminal coding assistant. Its edit tool uses a nested JSON array with `oldText`/`newText` fields.
- **Claude Code** is Anthropic's closed-source official coding assistant. Its edit tool uses a flatter schema and silently repairs malformed tool calls (unknown keys, aliases, Unicode glitches).
- Newer Claude models (Opus 4.8, Sonnet 5) sometimes add made-up keys (e.g. `requireUnique`, `oldText2`) to Pi's edit objects, causing rejection — though the actual edit is correct. Older models didn't do this.
- Likely cause: Anthropic's reinforcement learning optimizes for Claude Code's forgiving schema. Models develop a strong prior for that shape, so alternative schemas become "off-distribution," causing hallucinated extra fields.
- The documented text-editor API differs from what Claude Code actually uses internally, making interoperability harder for third-party harnesses.
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