liangzhidanta/claude-code-glm53-swesmith-trajectories

Claude-Code-native Coding Agent Teacher Trajectories (GLM-5.3 × SWE-smith)

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A private research archive of execution-verified, multi-turn coding-agent trajectories. A strong teacher (GLM-5.3) drives a real coding-agent harness (Claude Code) inside verified Docker environments derived from SWE-smith tasks; every trajectory is graded in a clean verifier container against the task's exact FAIL_TO_PASS / PASS_TO_PASS tests.

⚠️ PRIVATE dataset. Raw wire traces contain Claude Code harness-generated system/tool context and excerpts from the source repositories — see docs/LICENSE_NOTES.md before any redistribution or public release.

Dataset Summary

Teacher model	GLM-5.3 (zhipu Anthropic-compatible endpoint)
Harness	Claude Code 2.1.258 (multi-turn tool use: Read/Edit/Bash/Grep/…)
Task source	SWE-smith (snapshot ea6d7173829c)
Target student	Qwen3-4B trained with slime v0.3.2 (3778dbf)
Verifier	clean Docker container, exact F2P/P2P + cheating checks

Core statistics (from data/metadata/, not hand-written)

Metric	Value
Task pool	59,136 tasks / 222 repos (SWE-smith full train split)
Sampled & terminal tasks	1,597 (deterministic seeds 20260915, 40/40/20 E/M/H, repo ≤ 5%)
Teacher attempted	1,207 (after environment gate)
Teacher solve rate	93.9% (1,134 solved / 1,207 attempted)
Usable SFT trajectories	1,003
Context-length excluded	131 (> 32,768 tokens; raw kept, never truncated)
Unique repos in SFT set	99 (max single-repo share 1.99%)
Difficulty (usable)	Easy 469 / Medium 392 / Harder 142
Cheating / timeout	0 / 0
Teacher API cost	12.87M input + 2.95M output tokens / 15,360 calls
Raw archive	~2.0 GB (1,208 trajectories, 14 deterministic .tar.zst shards)
Median trajectory	23,389 total tokens / 1,598 trainable tokens (6.8% trainable)

Note the three distinct counts — 1,597 terminal tasks ≠ 1,134 teacher-solved ≠ 1,003 usable SFT — the funnel (environment gate → solve → context gate) is in docs/DATA_PIPELINE.md.

Where does the data come from?

1. SWE-smith provides the software-engineering tasks: problem statements, injected-bug patches, repository/environment definitions (official conda-pinned envs, rebuilt locally as repo-level Docker images).
2. Claude Code harness orchestrates the agent loop: tool invocation, context management, runtime reminders (<system-reminder>, <total_tokens>).
3. GLM-5.3 (Teacher) generates all assistant content: reasoning, text, tool calls.
4. Docker executes the real code operations (Read/Edit/Bash on the actual repository).
5. Clean verifier (separate Docker B) replays the bug baseline, applies only the agent's git diff, runs exact FAIL_TO_PASS / PASS_TO_PASS tests and cheating checks → execution-verified trajectories (every usable row has reward = 1.0).

Uses

• Coding-agent SFT (primary; see usage below)
• Tool-use behavior modeling / harness behavior analysis
• Agent trajectory research: multi-turn credit assignment, failure mining (all failed/oversized trajectories are archived in raw)
• Agent data flywheel seeding

Not recommended: feeding raw wire JSONL directly as generic instruction-tuning data. Use data/sft/teacher_v2_candidates.jsonl for training — it is the derived, schema-stable training view.

How to train with slime (validated on slime v0.3.2)

python train.py \
   --rollout-function-path slime.rollout.sft_rollout.generate_rollout \
   --prompt-data /path/to/data/sft/teacher_v2_candidates.jsonl \
   --input-key messages \
   --loss-type sft_loss \
   --calculate-per-token-loss \
   --disable-compute-advantages-and-returns \
   --loss-mask-type qwen3 \
   ... # your model/parallel/optimizer args, e.g. scripts/run-qwen3-4B-base-sft.sh

Loss-mask semantics (generated at training time, this dataset ships no pre-tokenized ids): system / user / tool observations (incl. harness reminders) → masked; assistant reasoning / text / tool_call / EOS → trainable. This exact configuration passed a full pipeline dry-run (forward → backward → optimizer step → checkpoint save → restart → resume) on Qwen3-4B.

Reading the data with plain Python (no slime required)

import json

with open("data/sft/teacher_v2_candidates.jsonl") as f:
    row = json.loads(f.readline())

msgs, meta = row["messages"], row["metadata"]
print("task_id:", meta["task_id"])
print("repo:", meta["repo"], "| difficulty:", meta["layer"])
print("reward:", meta["reward"], "| total_tokens:", meta["total_tokens"])
print("num messages:", len(msgs), "| num tools:", len(meta["tools"]))
for m in msgs[:6]:
    extra = f" tool_calls={[t['function']['name'] for t in m['tool_calls']]}" if m.get("tool_calls") else ""
    print(f"  {m['role']:9s} {str(m.get('content'))[:60]!r}{extra}")

# locate this trajectory's raw wire archive
idx = [json.loads(l) for l in open("data/metadata/raw_index.jsonl")]
hit = next(r for r in idx if r["task_id"] == meta["task_id"])
print("raw shard:", hit["raw_shard"], "path:", hit["raw_internal_path"])

Trajectory visualization

Open examples/example_trajectory.html (static, self-contained) to walk one reward=1 trajectory: Task → Teacher reasoning → tool call → tool result → Edit → Test → Verifier → Reward, including a per-message SFT mask view (gray = masked context, green = trainable assistant spans).

Regenerate for any trajectory with tools/trajectory_viewer.py (see tools/README.md).

Repository layout

data/sft/        teacher_v2_candidates.jsonl (1,003 rows) + pilot_train/val_v1 (59-row pilot split)
data/raw/        raw-000NN.tar.zst (14 shards, deterministic, byte-identical to source)
data/metadata/   raw_index.jsonl · candidate_manifest.json · production summaries/results
tools/           raw_to_sft.py · validate_sft_data.py · trajectory_viewer.py · build_sft_v1.py (+ config_example)
docs/            DATA_FORMAT.md · DATA_PIPELINE.md · LICENSE_NOTES.md
examples/        example_trajectory.html
publication_audit.json · RELEASE_MANIFEST.json

Limitations

• Teacher = GLM-5.3: the data carries the teacher's behavior/style bias.
• Harness = Claude Code 2.1.258: trajectories are native to this harness version (incl. its runtime reminders and repeated system/tool context).
• Success-biased: official SFT candidates are execution-verified successes and do not represent the natural failure distribution (failures are archived in raw for mining).
• Trajectories longer than 32,768 tokens are excluded from the SFT view (131 rows; kept in raw) — long-horizon behavior is under-represented.
• Harder-difficulty retention is lower than the 20% sampling target (14.2% of usable) due to environment failures and context exclusions concentrating there.
• Source-repository licenses are heterogeneous (docs/LICENSE_NOTES.md).
• This dataset is not a benchmark; do not use it to claim agent rankings.
• A repo-level held-out evaluation pool was frozen separately and is not included in this release (no task answers, teacher solutions, or gold patches of those tasks are present anywhere in this repository).