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This paper diagnoses and proposes SoftClamp, a calibration method that reduces tool-call boundary drift in multi-teacher on-policy distillation for agentic language models, decreasing over-calling while maintaining accuracy.
Discusses workarounds for tool-call failures and looping issues in Qwen3.6-27B model.
This is the sixth article in the series, explaining in detail the concept of subagent, its working principles, and its role in coding agents, including tool call and runtime mechanisms, as well as the applicable scenarios of different subagent types (fresh child, forked child, partial fork).
GLM-5.2 uses a technique to counteract reward hacking by detecting and blocking suspicious tool calls rather than penalizing the model, which prevents obfuscation seen in other methods.
OKed is a plugin for OpenClaw that intercepts risky tool calls and requires user approval before execution, preventing agents from performing destructive actions like deleting data or sending payments.
User reports encountering infinite tool call loops when using qwen3.6-27b, despite adjusting parameters like temperature and top-k.
The article questions why quantization benchmarks focus on perplexity and prose quality instead of tool call validity, arguing that structured outputs degrade earlier due to fewer valid token continuations, which could mislead practitioners about usable quant levels for agentic use.