glm-5.2-coding-and-debugging-traces

GLM 5.2 Agent Traces 207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL Generated by **moonshiner** — an open harness for distilling verified instruction-following, tool-use, and agentic coding traces. Behavior-preserving **instruction-following, tool-use, and agent trajectories** from **GLM 5.2** (`glm-5.2`). The category and row-share tables below describe the actual mix seen during training rather than assuming a particular task domain. This is an actively growing dataset. More is coming**: additional training programs and substantially more sessions will be added to this same repo. What makes it different **All real model trajectories.** Every row is a cumulative prefix of a genuine GLM 5.2 session captured end-to-end. The agent's causal exploration, tool arguments, results, corrections, and final responses are retained. **One next step per row.** A trajectory with N assistant turns produces N rows. Row k contains the complete context through assistant turn k; that final assistant message is the sole training target. **Runtime-normalized.** Runtime plumbing, UI decoration, control sequences, and verbose success boilerplate are removed or canonicalized while causal context remains. **Independently verified.** Coding sessions must pass deterministic tests and protected-file checks. Instruction-following sessions must pass deterministic tool-call, staging, argument, and response-constraint checks. Every retained trajectory also clears independent review. **Reasoning-effort step-down.** Failed trace attempts proceed through `xhigh → medium → low` (up to the configured attempt count) and stop at the first judge-accepted trace. If higher reasoning fails a task that lower reasoning succeeds on, the lower-effort trace is retained. Task mix High-level training programs, calculated from accepted trajectories using the same program mapping published in the seed catalog: | kind | trajectories | share | row share | flavor | |---|---:|---:|---:|---| | Buildin