LingweiGu/openswe-success-sft-v1-untokenized

Open-SWE-Traces successes — cleaned, untokenized (v1)

This is the untokenized companion of LingweiGu/openswe-success-sft-v1. It holds the same 81,963 resolved software-engineering agent trajectories from nvidia/Open-SWE-Traces (revision f8fb5b3d), with the same filtering, the same train/validation split and the same row order. Here they are chat messages for any tokenizer or chat template; the MiniCPM5 input_ids/labels are left out. Rows join to the tokenized release on trajectory_id (and content_sha256).

Split	Trajectories	Unique tasks	Repositories	Messages	Tool calls	MiniCPM5 tokens	MiniCPM5 supervised tokens
train	77,840	18,372	2,762	10,333,538	5,127,717	3,591,261,499	1,116,235,128
validation	4,123	872	145	546,323	271,093	191,911,168	58,345,014

Validation is repository-disjoint from train. Several successful attempts per task are kept (up to 18, mean 4.2), so rows are not independent tasks; cap or balance per instance_id when mixing.

Schema

Column	Content
trajectory_id, instance_id, repo, language, harness, teacher, source, source_shard, source_row, resolved	provenance (source_shard/source_row locate the upstream record)
messages	list of {role, content, reasoning_content, tool_calls, tool_call_id}; role ∈ system/user/assistant/tool; absent fields are null
messages[].tool_calls	list of {id, type: "function", function: {name, arguments}}; arguments is a JSON string (OpenAI format)
tools_json	tool definitions (OpenAI function schema list) as a JSON string
num_messages	number of messages
normalization_flags_json	per-row record of the normalizations below
content_sha256	SHA-256 of the conversation without call ids (same value as the tokenized release)
minicpm5_tokens, minicpm5_supervised_tokens	lengths under the MiniCPM5 template, for rough length filtering; other tokenizers differ

Harnesses are the upstream OpenHands (4,266 train), SWE-agent (40,402) and mini-swe-agent (33,172) formats, not converted to a common one: the system prompt and tool set vary by harness. Train teachers: Qwen3.6-27B 55,078, MiniMax-M2.5 12,645, Qwen3.5-122B-A10B 5,862, Qwen3.8-27B 4,255.

Use

import json
from datasets import load_dataset

ds = load_dataset("LingweiGu/openswe-success-sft-v1-untokenized", split="train")

def to_chat(row):
    """Messages and tools ready for tokenizer.apply_chat_template(messages, tools=tools)."""
    messages = []
    for m in row["messages"]:
        m = {k: v for k, v in m.items() if v is not None}
        if "tool_calls" in m:
            m["tool_calls"] = [{**c, "function": {**c["function"], "arguments": json.loads(c["function"]["arguments"])}}
                               for c in m["tool_calls"]]
        messages.append(m)
    return messages, json.loads(row["tools_json"])

messages, tools = to_chat(ds[0])

to_chat turns arguments into objects, which templates that render arguments key by key expect; keep the string if your template wants a string. For SFT, put loss on assistant turns (reasoning, text and tool calls) and treat system, user and tool messages as context. Trajectories go up to 131,072 MiniCPM5 tokens; do not truncate them silently.

How the text relates to upstream

messages is the cleaned upstream conversation, not a decode of token ids. We compared every row with its upstream record. Roles, message text, reasoning, tool names, argument values, upstream call ids and tool definitions are identical in all 81,963 rows. The only changes are these normalizations from the curation pipeline:

Change	Count
Tool-call arguments parsed from JSON strings; keys sorted (stored here again as a JSON string)	5,398,810 calls (all)
Empty reasoning_content ("") dropped	4,454,730 messages
Tool results linked to their call: upstream tool messages carry no tool_call_id, so each gets the id of the single pending call (conversations with several calls pending and no ids were excluded during curation)	5,316,847 results

Upstream metadata outside the conversation (such as the reference patch) is not included. Filtering and audits are described in the tokenized release and its provenance/.

Verification

Checked for every row before release:

1. Round-trip: converting each row back (null fields dropped, arguments parsed) gives exactly the conversation stored in the tokenized release (messages_json). tools_json is byte-identical, and row order and trajectory_ids match.
2. Tokens: rendering each row with the MiniCPM5-2B-Midtrain chat template (revision 0a45344e, assistant-only masking as in the curation pipeline) reproduces the released input_ids and labels exactly. That covers 81,963/81,963 rows and 3,783,172,667 tokens. content_sha256 recomputed from this data matches in every row.
3. Upstream: each row matches its nvidia/Open-SWE-Traces record up to the normalizations above (0 rows with any other difference).

License and attribution

Derived from nvidia/Open-SWE-Traces (CC BY 4.0; per-record upstream repository licenses apply, see the upstream card) and its task sources Scale-SWE and SWE-rebench-V2. Attribution to NVIDIA and the upstream authors.