Performance#

RPent Leaderboard

LIBERO-PRO

MethodOverallSpatial TaskSpatial SwapObject TaskObject SwapGoal TaskGoal SwapLong TaskLong Swap
RPent / GPT-6 Astra[4]Codex · low · reasoning 92.63% (741/800) 100.00% (100/100) 98.00% (98/100) 100.00% (100/100) 99.00% (99/100) 88.00% (88/100) 99.00% (99/100) 85.00% (85/100) 72.00% (72/100)
RPent / DeepSeek V4.1 Flash[5]Codex · xhigh · reasoning 90.00% (720/800) 98.00% (98/100) 98.00% (98/100) 100.00% (100/100) 100.00% (100/100) 90.00% (90/100) 93.00% (93/100) 74.00% (74/100) 67.00% (67/100)
RPent / DeepSeek V4.1 Flash[6]Codex · xhigh · reasoning 88.63% (709/800) 99.00% (99/100) 90.00% (90/100) 99.00% (99/100) 100.00% (100/100) 79.00% (79/100) 98.00% (98/100) 70.00% (70/100) 74.00% (74/100)
RPent Flash Mode[2]Molmo2-8B · visual localization 72.63% (581/800) 79.00% (79/100) 70.00% (70/100) 86.00% (86/100) 93.00% (93/100) 74.00% (74/100) 65.00% (65/100) 60.00% (60/100) 54.00% (54/100)
RPent / Qwen3.6 27Bno-reasoning 70.63% (565/800) 82.00% (82/100) 78.00% (78/100) 83.00% (83/100) 84.00% (84/100) 68.00% (68/100) 68.00% (68/100) 61.00% (61/100) 41.00% (41/100)
RPent / GPT-5.5Codex · xhigh · reasoning 75.13% (601/800) 81.00% (81/100) 69.00% (69/100) 94.00% (94/100) 91.00% (91/100) 75.00% (75/100) 66.00% (66/100) 70.00% (70/100) 55.00% (55/100)
RPent / GPT-6 Motor Only[3]Codex · low · reasoning — — — — — — — 38.00% (38/100) —
π0 0.30% 0.00% 0.00% 0.00% 2.00% 0.00% 0.00% 0.00% 0.00%
π_RLinf 50.00% 42.00% (42/100) 59.00% (59/100) 71.00% (71/100) 78.00% (78/100) 45.00% (45/100) 42.00% (42/100) 49.00% (49/100) 14.00% (14/100)
AtomVLA 6.30% 1.00% 16.00% 0.00% 10.00% 11.00% 2.00% 9.00% 1.00%
π0.5 11.00% 1.00% 20.00% 1.00% 17.00% 2.00% 38.00% 1.00% 8.00%
MolmoAct 1.50% 0.00% 0.00% 0.00% 6.00% 0.00% 0.00% 6.00% 0.00%
X-VLA 3.80% 0.00% 0.00% 8.00% 2.00% 9.00% 1.00% 10.00% 0.00%
Cap-X — 14.00% 12.00% 18.00% 22.00% 17.00% 26.00% — —
RATS — 31.00% 29.00% 63.00% 61.00% 36.00% 43.00% — —
RPent / Opus-4.7Claude Code · max · reasoning 82.40% (659/800) — — — — — — — —
ASPIRE[1] 61.36% 60.00% (300/500) 51.00% (255/500) 95.00% (475/500) 98.00% (490/500) 45.00% (225/500) 81.00% (405/500) 38.30% (192/500) 22.60% (113/500)

* [1] ASPIRE: Long Task and Long Swap use zero-shot transfer from the LIBERO-90 skill library.

* [3] RPent / GPT-6 Motor Only uses motor-only control: it directly outputs end-effector pose increments and gripper commands through execute_action, without invoking VLA / primitives or loading memory.

* [4] GPT-6 Astra (memory-enabled configuration): Long Task/Swap and the other six suites use separate memory-file snapshots frozen after their respective exploration phases, with no updates during evaluation. Overall combines two non-overlapping batches: Long 157/200 plus the other suites 584/600, giving 741/800 (92.63%); the 800 episodes do not share a single memory snapshot.

* [5] DeepSeek V4.1 Flash evaluated with memory explored by GPT-6 Astra.

* [6] DeepSeek V4.1 Flash evaluated with its own seed-0 exploration memory.

LIBERO

MethodOverallSpatialObjectGoalLong
RPent / Opus-4.7Claude Code · max · reasoning 96.00% (384/400) 97.00% (97/100) 100.00% (100/100) 94.00% (94/100) 93.00% (93/100)
OpenVLA 76.50% 84.70% 88.40% 79.20% 53.70%
NORA 79.50% 85.60% 89.40% 80.00% 63.00%
π0 94.20% 96.80% 98.80% 95.80% 85.20%
π_RLinf 95.30% 99.00% (99/100) 96.00% (96/100) 97.00% (97/100) 89.00% (89/100)
AtomVLA 97.00% 96.40% 99.60% 97.60% 94.40%

RoboCasa365 · Target50

MethodOverallAtomic-SeenComposite-SeenComposite-Unseen
RPent / GPT-6 AstraCodex · high · reasoning 59.20% (201/340) 87.78% (158/180) 43.75% (35/80) 42.50% (34/80)
RPent / GPT-5.5Codex · xhigh · reasoning 57.10% (194/340) 92.00% (166/180) 61.00% (49/80) 13.80% (11/80)
RPent / Opus-4.7Claude Code · max · reasoning 48.60% (165/340) 79.40% (143/180) 47.50% (38/80) 15.00% (12/80)
π0 14.80% 34.60% 6.10% 1.10%
π0.5 16.90% 39.60% 7.10% 1.20%
RLDX-1 30.00% 60.00% 21.30% 5.00%
WorldDreamer 35.30% 66.30% 26.70% 9.00%
Xiaomi-Robotics-1 57.40% 80.20% 57.10% 32.10%

RoboTwin

MethodC2R
RPent / GPT-5.5Codex · xhigh · reasoning 62.40% (156/250)
RPent / Opus-4.7Claude Code · max · reasoning 58.40% (146/250)
π0.5 47.90%
GR00T-N1.7 20.70%
StarVLA 10.60%
LingBot-VLA 50.40%

* RPent results on this leaderboard come from different evaluation dates; each source note identifies its run.

* LIBERO / LIBERO-PRO: all VLA-based RPent methods use the same RLinf-Pi05-LIBERO-130-fullshot-SFT checkpoint, released by RLinf, as the pi_RLinf baseline. This checkpoint was supervised-fine-tuned on LIBERO-130; RPent does not further fine-tune it for LIBERO-PRO, and its weights remain frozen during evaluation. Motor Only does not use a VLA.

* RoboCasa365: RPent uses RLDX-1-FT-RC365 released by RLWRLD, not an RLinf checkpoint. It was fine-tuned on RoboCasa365; RPent does not further fine-tune its weights and keeps them frozen during evaluation.

* RoboTwin: RPent uses LingBot-VLA-RoboTwin-EEF-ckpt1500 released by RLinf, a LingBot-VLA checkpoint supervised-fine-tuned on RoboTwin data. RPent and the direct LingBot-VLA baseline use the same checkpoint with frozen weights during evaluation.

* Methods may differ in training data, exploration / memory, inference budgets, and result selection. External results follow their original sources; the environment-specific VLA notes identify same-checkpoint comparisons.

* [2] RPent Flash Mode uses directly downloaded, officially released GPT-5.5 exploration memory, Molmo2-8B for visual localization, and the same VLA as pi_RLinf for action execution. The displayed scores are the best evaluation results from s0-s9.

RPent Leaderboard