rpt
sap-rpt-1-ossa3-rl-DCAgent_exp_rpt_unitsyn-python-v3-10-8Ba3-rl-DCAgent_exp_rpt_pymethods2test-v3-10-8Ba3-rl-DCAgent_exp_rpt_curriculum-easy-21-8Ba3-rl-DCAgent_exp_rpt_e2egit-v2-10-8BRPT-DeepSeek-R1-0528-Qwen3-8B-i1-GGUFColdBrew-Nemo-12B-Arcane-Fusion-RPThink0-i1-GGUFRPT-DeepSeek-R1-0528-Qwen3-8B-GGUF
Datasets
All datasets matching “rpt”berkeley_rpt_lerobotThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "franka",
"total_episodes": 908,
"total_frames": 392578,
"total_tasks": 4,
"total_videos": 908,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:908"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/IPEC-COMMUNITY/berkeley_rpt_lerobot.exp_rpt_multifile-a0-base-pass8-300
TaskTrove A0 base pass@8 cohort
This repository contains the 300 extracted Harbor tasks selected for the A0
base-model pass@8 evaluation. The source is DCAgent/exp_rpt_multifile at
revision 70527e80ee7497c800ea0f9bad90b87423c784c9.
cohort.json records the deterministic random selection algorithm, seed, and
complete task list. The task directories are the unmodified archives from the
source dataset.
berkeley_rpt_rawberkeley_rptThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.0",
"robot_type": "unknown",
"total_episodes": 908,
"total_frames": 392578,
"total_tasks": 4,
"total_videos": 908,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:908"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/lerobot/berkeley_rpt.exp_rpt_pymethods2test-large-qwen3.5-122b-131k-opencode-traces
Agent trace dataset
Decoding the literal token IDs
The prompt_token_ids / completion_token_ids / logprobs columns are the
verbatim tokens the serving engine emitted, stored PER AGENT STEP as a
list-of-lists (one inner list per turn). To turn them back into text you MUST
use the exact tokenizer the model was served with — a generic same-family
tokenizer will decode word tokens to garbage.
Served model / tokenizer source: Qwen/Qwen3.5-122B-A10B-FP8
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/exp_rpt_pymethods2test-large-qwen3.5-122b-131k-opencode-traces.a3-rl-DCAgent_exp_rpt_pymethods2test-large