datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
harbor-swesmith-rl-artifacts
Harbor SWE-Smith 强化学习数据产物
本数据集是 Harbor Qwen 工具调用代码智能体强化学习项目使用的冻结任务集，服务于 GRPO、原生价值模型/GAE PPO、训练过程诊断和统一协议评测。
项目已于 2026 年 8 月 30 日完成 P0 评测并进入阶段性归档。本数据集用于保留实验所依赖的数据切分、任务执行文件和审计信息，不代表新的通用代码能力基准。
数据概况
切分
任务数
训练集
187
验证集
42
测试集
38
合计
267
数据覆盖 89 个上游代码仓库。三个切分之间同时执行任务标识和仓库级隔离检查。
正式数据集名称：
swesmith-curated-grpo-267-v1
冻结切分的语义摘要：
ae5df9a3f4a3fc8af44fac420b36529e283839e1bd3de9daba65d5bcda51447d
该值来自 split-manifest.json 的 sha256 字段，用于标识切分语义，不等同于该文件本身的字节级… See the full description on the dataset page: https://huggingface.co/datasets/keryszhan/harbor-swesmith-rl-artifacts.swe_smith_rebenchv2_5136
SWE-smith + SWE-rebench V2 5136 Mix
This dataset is the swe_smith_rebenchv2_5136 training mix used by the rLLM SWE training scripts. It combines filtered SWE-smith trajectory tasks with sampled SWE-rebench V2 tasks so future training jobs can pull the prepared parquet directly instead of regenerating it with the long preparation script.
Contents
data/train.parquet: the canonical rLLM task rows, 5,136 examples.
rllm_verl/train.parquet: the rLLM DatasetRegistry… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/swe_smith_rebenchv2_5136.swe-smith-frozen-trajectories-openai
SWE-Smith Frozen Trajectories — OpenAI Wire Format
This dataset is the OpenAI chat-completions wire-format release of
reflectio/swe-smith-frozen-trajectories,
derived from the tool split of
SWE-bench/SWE-smith-trajectories.
It is a serving-performance workload for realistic multi-turn coding-agent
histories. It can be used to measure request throughput, input/output token
throughput, TTFT, TPOT, streaming behavior, and prefix-cache reuse. It is not
a coding-correctness… See the full description on the dataset page: https://huggingface.co/datasets/reflectio/swe-smith-frozen-trajectories-openai.swe-smith-frozen-trajectories
SWE-Smith Frozen Trajectories
This dataset is a serving-performance workload derived from the tool split of
SWE-bench/SWE-smith-trajectories.
It is designed for measuring throughput, request rate, time to first token,
inter-token latency, and prefix-cache behavior with realistic multi-turn coding
agent histories.
It is not a coding-correctness benchmark. The tested model's responses are
not executed or scored.
Processing
Keep trajectories generated by… See the full description on the dataset page: https://huggingface.co/datasets/reflectio/swe-smith-frozen-trajectories.agent-trajectories-swesmith-random-subset
Agent Trajectories: SWE-Smith Random Subset — Single Model
Summary
Full multi-turn agent trajectories collected from a random subset of SWE-Smith issues,
generated with a single model per trajectory (no branching or lookahead).
Contains a resolved column from SWE-bench evaluation.
Intended for SFT of agent models on diverse, synthetically generated coding tasks.
Data Collection
Each trajectory was produced by a mini-swe-agent run with an LLM router selecting… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/agent-trajectories-swesmith-random-subset.