VanishD/agentic-sft

Agentic SFT trajectories (ARC-AGI-2 + frontier-cs-algo)

All corpora under train/data/ as one dataset. Each subdirectory is one build (task × rollout context × selection × training cap). The published single-corpus set VanishD/arc-agi-2-agentic-sft is the arc2_95k_win_128k_old slice of this tree.

Live builds (no _old suffix): 318,497 samples across 6,060 trajectories. A sample is one assistant turn plus the exact context that turn was produced from.

Corpora

corpus	cap	select	samples	traj	max tokens	ctx_trimmed
arc2_48k_all-s0_64k	65536	all	19,766	588	65,531	33
arc2_48k_all-s50_64k	65536	all (floor=0.5)	15,173	547	65,531	16
arc2_48k_best-s0_64k	65536	best	14,626	586	65,501	10
arc2_48k_best-s50_64k	65536	best (floor=0.5)	12,380	547	65,501	6
arc2_95k_win_128k	131072	win	28,408	952	116,528	0
arc2_95k_win_32k	32768	win	28,333	952	32,768	8,141
arc2_95k_win_72k	73728	win	28,405	952	73,726	766
fcs_48k_all-s0_64k	65536	all	23,391	91	65,398	32
fcs_48k_all-s50_64k	65536	all (floor=50.0)	14,884	61	65,370	11
fcs_48k_best-s0_64k	65536	best	5,474	91	65,313	7
fcs_48k_best-s50_64k	65536	best (floor=50.0)	3,486	61	65,313	3
fcs_95k_all-s0_100k	100000	all	26,896	94	100,000	2,021
fcs_95k_all-s0_128k	131072	all	26,899	94	126,997	0
fcs_95k_all-s50_100k	100000	all (floor=50.0)	16,278	59	100,000	1,190
fcs_95k_all-s50_128k	131072	all (floor=50.0)	16,280	59	126,997	0
fcs_95k_best-s0_100k	100000	best	10,088	94	99,999	725
fcs_95k_best-s0_128k	131072	best	10,090	94	126,997	0
fcs_95k_best-s50_100k	100000	best (floor=50.0)	6,298	59	99,999	404
fcs_95k_best-s50_128k	131072	best (floor=50.0)	6,299	59	126,997	0
fcs_95k_eval20_128k	131072	all	5,043	20	114,188	0

_old builds predate require_tool_call and are kept only to reproduce older numbers:

corpus	cap	select	samples	traj	max tokens	ctx_trimmed
arc2_95k_win_128k_old	131072	win	29,358	952	116,528	0
arc2_95k_win_32k_old	32768	win	29,283	952	32,768	8,431
arc2_95k_win_72k_old	73728	win	29,355	952	73,726	806

Naming: <task>_<rollout-ctx>_<selection>_<train-cap>. _ separates the four fields; - joins inside one field. Score floors: ARC is 0–1 so s50 means min_score=0.5; fcs is 0–100 so s50 means min_score=50.

Format

JSONL shards named *.jsonl.shardKofN (not .jsonl, so load_dataset will not auto-parse them). One sample per line:

{
  "messages": [ {"role": "system", "...": "..."}, ...,
                {"role": "assistant", "...": "THE TRAINING TARGET"} ],
  "n_ctx": 12,
  "n_tokens": 21254,
  "task_id": "arc_agi_2_00576224",
  "source": "student",
  "iter": "iter_0001",
  "ctx_trimmed": false
}

Token ids are not stored. Render with the Qwen3.5 chat template at load time and pass tools.json (four functions: Bash / Read / Write / Edit) — it is part of the context the agent saw.

from pathlib import Path
import json
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer

root = Path(snapshot_download("REPO_ID", repo_type="dataset"))
tools = json.loads((root / "tools.json").read_text())
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")

def rows(corpus: str):
    for p in sorted((root / corpus).glob("*.jsonl.shard*")):
        if p.name.endswith(".manifest.json"):
            continue
        with p.open() as f:
            for line in f:
                yield json.loads(line)

sample = next(rows("arc2_48k_best-s0_64k"))
ctx  = tok.apply_chat_template(sample["messages"][:sample["n_ctx"]], tools=tools, tokenize=False)
full = tok.apply_chat_template(sample["messages"], tools=tools, tokenize=False)

Every shard has a sibling .manifest.json with counts, rejects, and sha256 pins of AGENT_SYSTEM.md / ralph_agent.py.

Two things to know before training

1. Reasoning is a target, never context. Prior-turn reasoning_content was dropped from history at inference. Only the final target turn in a sample carries it. Reintroducing think blocks into context inflates length ~30% and diverges from inference.

2. Teacher (GLM-5.2-Int4) reasoning is sparse. Empty reasoning_content renders as a degenerate <think></think> and trains "act without thinking". Do not mix student and teacher naively.

Licence

ARC-AGI-2 task content: arcprize/ARC-AGI-2. frontier-cs-algo: the FCS release licence. Model outputs: Qwen3.5 and GLM-5.2 terms. Grids and held-out test answers are not included.