ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
Authors: Chi-Pin Huang
Yueh-Hua Wu
Min-Hung Chen
Yu-Chiang Frank Wang
Fu-En Yang
Venue: NeurIPS 2025 poster ()
Keywords: Vision-Language-Action Models
Reasoning
Primary area: applications

Abstract: Vision-language-action (VLA) reasoning tasks require agents to interpret multimodal instructions, perform long-horizon planning, and act adaptively in dynamic environments. Existing approaches typically train VLA models in an end-to-end fashion, directly mapping inputs to actions without explicit reasoning, which hinders their ability to plan over multiple steps or adapt to complex task variations. In this paper, we propose ThinkAct, a dual-system framework that bridges high-level reasoning with low-level action execution via reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate embodied reasoning plans guided by reinforcing action-aligned visual rewards based on goal completion and trajectory consistency. These reasoning plans are compressed into a visual plan latent that conditions a downstream action model for robust action execution on target environments. Extensive experiments on embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct enables few-shot adaptation, long-horizon planning, and self-correction behaviors in complex embodied AI tasks.

ThinkAct: Vision-Languagg-Actionn Reasoning via Reinforced Visual Latent Planning
Chi-Pin Huangl.2
Yueh-Hua Wu'
Min-Hung Chen Fu-En Yang'
Yu-Chiang Frank Wang
National Taiwan University {chipinh, krisw, minhunge, frankwang, fredy) com
NVIDIA
Abstract
Vision-language-action (VLA) reasoning tasks require agents to interpret multi- modal instructions, perform long-horizon planning, and act adaptively in dynamic environments. Existing approaches typically train VLA models in an end-to-end fashion, directly mapping inputs to actions without explicit reasoning, which hin- ders their ability to plan over multiple steps or adapt to complex task variations. this paper, we propose ThinkAct dual-system framework that bridges high-level reasoning with low-level action execution via reinforced visual latent planning. ThinkAct trains multimodal L.M to generate embodied reasoning plans guided by reinforcing action-aligned visual rewards based on goal completion and trajec- tory consistency. These reasoning plans are compressed into visual plan latent that conditions a downstream action model for robust action execution on target en- vironments. Extensive experiments on embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct enables few-shot adaptation. long-horizon planning, and self-correcctio behaviors in complex embodied AI tasks. Project page: https::/jasperr331 1--hoerrrroohuuuugg hww..gggihhubbbi//inaaaa.t.....
1
Introduction
Recent advances in multimodal large language models (MLLMs) 44 25 2
27 56
22 6
have led to impressive progress on various tasks requiring the understanding of multimodal inputs, such as visual question answering and image/video captioning. However, while multimodal content can now be effectively perceived and interpreted, conducting multi-step planning for long- horizon user goals and then interacting with dynamic environments remains challenging for frontier MLLMs. Therefore, enabling the iisiun-anggugg foundation models with action awareness and embodied reasoning capabilities unleashes wide range of physical AI applications (e.g., robotics and AR assistance), and draws significant attention from both academics and industry.
To bridge action with vision-language modalities, several works 16
55
3
45
learn vision-
language-action (VLA) models by initializing from pre-trained MLLMS and training on large-scale
robotic demonstrations (e.g., Open X-Embodiment Dataset (33). For example, OpenVLA [66 builds
upon MILMS with post-training on large-scale robot demonstrations, while TLA (55) further
applies visual traces prompting to enhance spatial understanding. Despite promising on short-horizon skills, the crucial capabilities reason diverse visual scenes and enable long-horizon planning remain limited due to the end-to-end fashion from visual and textual inputs to low-level actions.
To equip VLAs with the ability to solve complex embodied tasks, recent works [52
10 54
40)
have explored incorporating explicit chain-of-thought (CoT) prompting 447 as an intermediate step-by-step guidance. For instance, ECoT [52] and RAD [10] introduce data curation pipelines to generate intermediate steps and decomposed plans by prompting off-the-shelf MLLMs. Once the
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Reinforcement Reasoning
Let's start by andivaina the imase and The task of
10-shot LIBERO
hand I'll identify the obiects the scene.
Few Sow-Shot Adaptation
There's basket, robot, can of ciphabet bottle, and a few other itemé The alphobet the floor, the robot will need to pickit up.........
Action Aligned Visual Feedback
Theekatt (Dun)
by step:
Trajactory Alignment
Long-Honizon, Planning
imsge shows ... to pick up Move the book smoothly from left
robof's orm near book
to right Place it in the back compartment This
logicol and fecsible plonning
The the streaberry the dnover?
Thinking
Acting
'Let's stort excmining the scens and the task
Drapped
Completion
Trajectory
Aigenee
X
Self Conrection
Hmm, let' reconsider how to complete this task,
çoal is However, something unexpected
cream dropped robet's arm.
Cornect
Reflet
Figure 1: We introduce ThinkAct, reasoning VLA framework capable of thinking before acting. Through reasoning reinforced by our action- aligned visual feedback, ThinkAct enables capabilities of few-shot adaptation, oong-hoizonn planning, and self-correction in embodied tasks.
annotated CoT traces are obtained, VLAs are trained predict intermediate steps via fully supervised fine-tuning (SFT). However, due to the high cost of producing resulting models are prone to overfitting to specific visual scenes or reasoning patterns.
reasoning traces, the
Recently, reinforcement learning (RL) [39 14 has demonstrated significant potential to incentivize reasoning behaviors LLMs by exploring the thinking trace that maximizes reward signals instead of solely relying on fully supervised CoT annotations. Inspired by this paradigm, several vision-language models 12
have applied RL-based reasoning to multimodal tasks. For example, Video- R1 12 adopts R I -style RL optimization to induce the CoT traces by verifiable answer accuracy with format correctness. While this manner enables long-form reasoning without step-level supervision, the reliance on QA-style reward signals limits their ability to support long-horizon planning and makes difficult to connect reasoning with real-world action execution.
31 43
In this paper, we propose ThinkAct, which aims to enable MILMS with the capability to reason before acting in physical environments. To address vision- language-actinnn reasoning tasks, ThinkAct adopts a dual-system architecture that connects structured reasoning with executable actions. Specifically, incentivize MLLMs perform long-horizon planning by advancing reinforcement learning with an action-aligned reward, derived from visual goal completion and trajectory distribution matching. Our Think Act leverages human and robot videos to elicit embodied reasoning that is grounded in visual observations. To bridge reasoning and execution, we compress intermediate reasoning steps into a compact latent trajectory that captures high-level intent and allows efficient adaptation of the downstream action network to new enviromments. By reinforcing structured reasoning and grounding it in real-war wal-w actions, ThinkAct tackles long-ho long-horizon manipulation tasks while unleashing few-shot action adaptation and self-correction behavior in physical Al scenarios, as shown in Fig.
Our main contributions summarized as follows:
We propose ThinkAct, dual-system framework that mutually enhances action execution and visual-grounded embodied reasoning connected by visual latent planning.
We leverage the visual feedback of goal completion and trajectory alignment as action- aligned rewards allow long-horizon reasoning grounded in the embodied scene.
We advance visual latent planning to steer downstream action execution by providing reasoning-enhanced enhanced trajectory guidance across diverse environments.
We demonstrate that our learned reasoning VLA enables capabilities of few-shot adaptation, long- loni-horizon planning, and self-correction across diverse embodied manipulation tasks.
Related Works
2.1
Vision-Langunge-Acttion Models
Recent efforts 19
have adapted large language models (LLLMs) and vision- vasion-language models (VLMs) for action-centric tasks by prompting post-training curated instruction-followe following
50
2

---

data. For example. RoboPrompt [50] is designed to prompt off-the- LLMs to predict robot actions by constructing in-context demonstrations. RoboPoint [5T] and LLARVA 30 leverage point and visual trajectory into textual prompts to augment L.MMs with spatial-action understanding ability. AHA [1] enhances failure detection ability in robotic manipulation by formulating it as a free-form question-answering task, training synthetic failure data generated by perturbing successful trajec- tories. Although effective in specific domains, these approaches depend on sophisticatedly curated data and struggle to generalize beyond their training distributions. To improve scalability, recent vision-language usioo-language-actiion (VLA) models 16 55 42 21 48] adopt large-scale robot datasets (e.g., Open X-Embbdiment Dataset [33 or DROID (15) to train models directly on diverse demonstrations. Open VLA [16] learns from pre-trained VLMs with robot trajectories for generalist action execution, while VLA [55] and HAMSTER 21] enhance spatial-action awareness by incorporating visual traces. However, these models predict actions directly from vision and language inputs, often bypass- ing structured planning intermediate reasoning. As a result, their capability to handle complex instructions, long-horizon goals, or out-of-distribation scenarios remains limited.
2.2 Reasoning in Vision-Languagee(Actinnn Models
of-in-of-thought (CoT) prompting 47 46 49] has significantly improved the multi-step reasoning ability of LLMs across math, coding, and question-answering tasks. Motivated by these advances, recent works extend reasoning capabilities to aasion-language-actiinn (VLA) models for embodied tasks. ECoT [52] synthesizes intermediate subgoals via prompting and applies supervised fine-tuning to teach VLAS to reason before acting. RAD (10) leverages action-fres free human videos to curate reasoning traces by prompting off sheel LLMs and learn map reasoning to real actions using robot data. On the other hand, COT- VLA (54) replaces linguistic CoT with visual subgoal frames generated ahead of action prediction. However, they depend on either curated CoT supervision or task- specific video generation, limiting their scalability. Inspired by the recent success of RL-oopimized reasoning models 33 14, several approaches 112
optimization to guide CoT generation in lanion- tasks using verifiable rewards. However, their QA-formatted rewards cannot fully support long-horie Long-horizon planning or establish grounding between reasoning and action execution. To unily structured CoT reasoning with embodied decision-making we introduce ThinkAct, which leverages action-al aaliind reinforcement learning and visual latent planning to connect embodied reasoning with real-world action in VLA tasks.
43 23
adopt GRPO
39
3 Method
3.1 Problem Formulation
We first define the setting and notations for usiionlaaguaage-attion (VLA) reasoning tasks. At each timestep the model receives visual observation o, and textual instruction I, with the goal of predicting an action a,, which can be a textual command or a 7-DOF control vector [A,, Ag. AGmi]] depending the embodiment. To tackle this problem, we propose ThinkAct, unified framework that aims to leverage an MILMM Fa to reason the high-level plans while connecting with an action model F. infer executable actions. The MLLM Fa produces visual plan latent e based on (o,, !). capturing the high-level intent and planning context (Sec. 3.2). This reasoned plan c, then guides the downstream action module Ty to sequentially predict N executable actions [ar](+> tailored to the target environment (Sec. 3.3 By connecting abstract planning with low-level control, our ThinkAct enables long-horizon reasoning and improves action adaptation in dynamic embodied tasks.
3.2 Reinforeed Visual Latent Planning for Embodied Reasoning
To enable embodied reasoning that generalizes across diverse environments, we aim to incentivize the reasoning capability of multimodal LLMs via reinforcement learning [39 [4] A straightforward way is to have the MILMM reason before generating low-level actions, while using the resulting task success rate in target environments (e.g., LIBERO (24) as the reward signal. However, this approach is restricted to specific simulators without proper guidance from visual scenes.
Reward Shaping from Action-Aligned Visual Feedback To tackle this challenge, we design novel action-aligned visual feedback that captures long-horizon goals and encourages visual
3

---

<thinlo
Fy reasons every steps
To the strowberry drower, the robot needs to: pick the strawberry 2. then put the picked strowberry... ...
Tg every step
Reasoning MLLM Fo
</thinlo
"Put the strawberry in the drower."
Octr
= Pl, P
State Encoder
attn
</answer>
Action Model T
A,
A
Addp
Action-Alignee Visual Reward
GRPO Optimization
Latent Projector
Tgual
Traj. Tasj
(f + fipa)
mmx F))
C₁
(a)
(b)
Figure 2: Overview of our ThinkAct. (a) Given observation on and instruction 1, ThinkAct advances action-aligned rewards derived from visual trajectory т to incentivize embodied reasoning capability of Reasoning MLLM Fg. (b) Conditioned on the visual plan latent cr, the DiT-based Action Model To learns to predict executable action while keeping F9 frozen. Note that, during inference, T& and Fg could operate asynchronously enable slow thinking and fast control for VLA reasoning tasks.
we are capable of grounding during planning. Specifically, inspired by recent works 48 representing high-level plans as over the visual scene, which serve as visual-action guidance to steer the embodied reasoning.
trajectories that capture the gripper end-effector
As depicted in Fig. (ia), given an observation or at timestep and a task instruction 1, the MLLM Fo autoregressively generates sequence of latent embeddings for reasoning 0 Rlo(Xxd X and visual plan e Rla|xdd where the former decoded to reasoning steps while the latter would be inferred into a text string of 2D points 7 = [p*]æ=>>> with pk: E 1]3, and #1 and pk denoting the start and end positions of the gripper. As result, encourage the model to anticipate visual goal completetion, we introduce the goal reward for comparing predicted start and end positions with corresponding
points from trajectory obtained by off-the off-the-shelf detector 30 f
[p
k=1 as follows,
1 (J (PI, + (pk, where f(p. max (0, - lp p plll)
Tgnal=
2
To further enforce the MLLM predicted trajectory to properly correspond to physically plausible gripper motion, the trajectory reward is proposed to regularize the predicted to match the distribution of demonstrated trajectory Thus, the trajectory reward Fuaj can be computed as follows,
Tay max (0, d(r, +))
Here, d(r, +) denotes metric measuring the distance between trajectories, i.e., dynamic time warping (DTW) distance 337 in this work.
The overall reward is thus defined the combination of proposed action- aalignd visual feedback and the format correctness score Tteemat following existing reasoning works B1 14:
r v. + 0.1 where Tvisual = Wyoat goal + WimjFinj-
Here, Wgoul Wurj 0.5 are the weighting coefficients for the goal and trajectory rewards.
Reinforced Fine- Fine-Tuning for Eliciting Visual Latent Planning To incentivize the embodied reasoning from the MLLM Fo. perform reinforced fin-tuning using Group Relative Policy Optimization (GRPO) (39]. Specifically, given an input (o,, 1), GRPO first samples a group of distinct responses {21 22, ..... 2M from the original MILM Feur. Each response is evaluated using the reward function defined in Eq. 3a.3aand resulting in a set of reward signals {ri, T2, JM). Thus, optimize Fg by maximizing the Tollowing objective:
Felzzlor,)) 1)
(G) = Σ
Ar 3Dxi(Fo(z(loe)) Fa (zi ())),
(4)
For (2, 00,

---

A. Ti mean T.M
where
std( (ri,... rMM)
Here, A, quantifies the relative quality of i-th response compared to other candidates in the sampled group. D -I is the KL divergence introduced with a weighting factor / to regularize the model, preventing excessive deviation from the original model Fe...
To further obtain general embodied knowledge, our ThinkAct is flexible to encapsulate the publicly available question-ans answering data enhance capabilities such robotic VQA B8 failure detection [26] by formatting them into the QA-style accuracy reward. Specifically, the QA-style accuracy reward is computed by either answer accuracy for multiple-choiee QA tasks averaged ROUGE- 1/2/L. scores for open-ended QA tasks, as mentioned in Supplementary Sec. B.1.1. Once we obtain the QA reward rQΛ- use the same approach as in Eq. that combines the QA-style reward with the format reward, and then optimize using GRPO Specifically for QA tasks, the total reward becomes: 0.9rqA O.
Once the reinforced fine-tuning is complete, we are able to produce long CoT steps, while abstracting the textual reasoning into compact visual plan latent ct. capturing long-horizon spatial-temporal planning intent.
3.3
Gaasoning-Enhaneee Action Adaptation
With the high-level embodied intent reasoned by the MILM, our goal is to connect the inferred visual latent planning e with the action model Te of the target environment in think-before-actio grounding embodied reasoning into the physical world with executable actions. Specifically, we build upon Transformer-basee action model F. (e.g.. Diffusion Policy (9), which predicts actions based on the current state composed of visual observations and language instructions. While Th can operate in the target environment using perception alone, we enhance its capability by conditioning it on the latent plan C22 which encodes high-level embodied intent and planning context.
manner,
As depicted in Fig. 2(b). incorporate using latent projector to connect it to the input space of the action model, enabling the reasoning guidance to be effectively leveraged, which enhances its low-level action execution in the target environment. Thus, solely update the state encoder, latent projector, and action model by imitation learning with annotated action demonstrations:
(Ø) E(,,,,a)) [e ( (ce, o,, (), a:)].
(5)
We note that, reasoning and action execution could be operated in an asyлchronous manner, which means each latent plan c, corresponds to N interactions with the environment (i.e., i E [t. ND. This asynchronous design highlights key advantage of our dual-system architecture, allowing the reasoning MILM to perform slow thinking while the action model executes fast control.
3.4 Learning Strategy and Inference
Following 31 we adopt multi-stage training strategy for our ThinkAct. Before RL, we initialize the two modules independently. The MLLMM Fø is cold-started using supervised data (Sec. 4.1 to learn to interpret visual trajectories and produce reasoning and answers in the correct output format. On the other hand, the action model ne is pre-trained on the Open X-Embodiment (OXE) dataset 33 providing a strong foundation for low-level action execution. After SFT cold-start, our MILM Fo tuned with action-aligned rewards guiding the generation of effective latent plans. During reasoning-ed -enanncee action adaptation, we freeze Fa while updating the action model n with state encoder and latent projector on the target cnvironment by conditioning on the latent visual plan et-
At inference time, given visual observation o and instruction I, ThinkAct produces visual plan latent a = Fo 1), which conditions the action module To to predict a sequence of executable actions tailored to the current environment.
Experiment
4.1 Experimental Setup
Implementation Details We initialize Fg with Qwen2.5-VI. 7B 22. The cold-start stage runs for
20K iterations with batch size 32 and learning rate 1e-5 using DeepSpeed ZeRO-3. We then apply
5

---

Table 1: Quantitative comparisons of robot manipulation tasks SimplerEnv 20 and LIBERO benchmarks. Bold denotes the best result.
24
Dataet
Split
Octo-Base [5
RT1-X
S
OpenVLA
[6
DiT-Policy [
TraceVLAA
55
COEVLA
51]
Magma [8]
ThinkAd
(Ours)
Open Drawer
1.0
22.5
49.5
11.9
57.0
56.0
50.0
Simpler-Gogle
53.7
(Visual Matching)
Pick Coke Can
1.3
52.8
15.3
64.3
28.0
92.0
Overall
1.8
41.4
37.3
56.0
46.2
68.4
71.5
Open Drawer
22.0
56.0
22.5
35.5
31,0
53.4
47.6
Simple-Google
(Viriant Aggregation)
Pick Ooke Can
17.0
54.0
52.8
$6.4
60.0
68.8
84.0
Overall
14.4
48.1
43.1
48.2
49.1
62.6
65.1
Put Carrot Plate
8.3
4.2
4,2
20.4
31.0
37.5
Simpler- Bridge (Visual Matcchnn))
Stack Blocks
0.0
0.0
0.0
12.7
Put Spuon
12.5
0.0
8.3
34.5
37.5
58.3
Put Fggplant Basket
43.1
0.0
45.8
68.5
6015
70.8
Overall
16.0
1.1
14.6
32.4
35.4
433.8
Spatial
78.9
84.7
84.6
87.5
88.3
Object
LIBERO
Goal
84.6
79.2
82.1
75.1
87.6
87.1
Long
51.1
53.7
57.6
54.1
69.0
70.9
Overall
75.1
76.5
74.8
83.9
84.4
GRPO 39] for 6K iterations, using batch size 64, learning rate 1e-6, and rollout size 5. The action model The is DiT-based policy [9] with 432M parameters, pre-trained using the OXE dataset [33
where the state encoder is composed of a DINOv2 image encoder 32 and CLIP text encoder (36
that jointly encode the current state inputs into 1024-dim embeddings. For reasoning-enhaneee action adaptation, we connect the visual plan e via a Q-Former [18] the latent projector with 32 queries and fine-tune on 100K data randomly sampled from the OXE dataset for 1 120K iterations using batch size 256 and learning rate 2. LIBERO 24 tasks are further fine-tuned for 75K iterations with batch size 128. All experiments are conducted on 16 NVIDIA A100 GPUs with 80 GB memory.
Training Datasets and Evaluation Benchmarks For SFT cold-start, we fine-tune the MLLM using trajectories from the subset OXE, and QA tasks from Robo VQA [38] EgoPlan-IT 771, and Iideo-R1-Col [12]. During RL training, we incorporate trajectories from the OXE subset and human videos from Nomething-Somettle embodied QA datasets such EgoPlan- IT/Val 77 RoboVQA 38] and the Reflect dataset (26). as
v2 [13]. To enhance general reasoning capability, we include
well as a general video instruction dataset, i.e., ILaVA-Video 178K 53
We evaluate ThinkAct on two robot manipulation and three embodied reasoning benchmarks. For manipulation tasks, SimplerEnv [00 containing diverse scenes and LIBERO [2] with long-horizon tasks are evaluated using task success rate. For reasoning benchmarks, EgoPlan- Pgoolan-Bench2 B35 uses accuracy on multiple-choit questions, while Robo VQA 338 and OpenEQA 29 are free-form QA tasks evaluated using BLEU score [34] and LLM based scoring, respectively, following their original protocols. Further details of our experimental setup are provided the supplementary material.
4.2 Quantitative Evaluation
Robot Manipulation To assess the effectiveness of ThinkAct on robot manipulation task, evaluate on SimplerEnv (20) and LIBERO (24). SimplerEnv (00 includes Google VM (Visual Matching), Google-VA (Variant Aggregation). and Bridge- setups, introducing variations in color, material, lighting, and camera pose to evaluate model robustness. For the LIBERO [24] benchmark, following prior works [16] (5).. evaluate the SEBERO--Sptian LIBERO-Object, LIBERO-GE -oal, and LIBERO-Long subtasks to test model generalization across spatial layouts, object variations, goal diversity, and long-horizon planning.
As shown in Tab. [ on the SimplerEny, incorporating our reasoning sessoning-guieed guided visual plan latents allows ThinkAct to outperform our baseline action model, DiT-Policy, by 15.5%, 16.9%, and 11.4% on Google VM, Google VA, and Bridge-VM, respectively, achieving the highest overall scores of 71.5%, 65.1%, and 43.8% against all methods. On the LIBERO benchmark, Think achieves the best overall success rate of 84.4%, outperforming DiT-Policy and recent otate-of-the-art CoT-VLA 54 verifying the effectiveness diverse manipulation settings.

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Table 2: Quantitative comparisons of embodied reasoning tasks GhoPlan-BBnchee RoboVQA, and OpenEQA benchmarks. Note that, Qwen2.5-VL indicates fine-tuning the original Qwen2.5-VL using EgoPlan-IT 7 and RoboVQA [8] datasets. Bold denotes the best result.
Split/ Metric
GPT-4V
LLaVA- Video [7
InternV1.225
1ntemV113 56
NVILA
Qwen2.-VV.
Qwea2.5-11. VI.*
ThinkAct
[8
(Ours)
Daily life
36.7
38.0
36.2
58.5
35.8
31.4
47.9
32.1
50.1
EgoPlan-
Bench2
Recreation
Hobbics
32.5
33.9
37.4
39.0
34.4
35.4
57.2
36.1
17.2
35.4
29.5
28.6
44.3
44.2
29.3
34.4
45.2
44.8
BLEU-1
32.2
35.4
40.5
44.3
42.7
47.8
65.3
38.6
69.1
BLEU-2
26.5
32.1
33.3
36.5
39.7
41.2
57.3
31.5
61.8
RoboVQA
24.7
29.6
37.6
28.1
BLEU-4
23.9
29.0
27.5
28.9
36.1
33.7
48.0
26.7
52.4
Overall
26.8
31.6
32.7
35.3
39.0
39.7
55.7
31.2
39.8
Obj. State
63.2
69.1
70.2
68.9
66.1
63.2
62.4
59.9
70.0
Obj. Recog.
43.4
42.6
47.2
49.1
49.5
46.2
45.2
43.8
47.2
Func. Reason.
OpenEQA
Spatial
33.6
46.2
44.1
43.3
43.1
41.2
42.8
39.3
47.6
Attri Recog.
57.2
64.1
64.9
74.4
69.3
63.0
65.0
58.3
71.1
Obj. Loc.
42.0
38.2
41.9
45.0
39.9
36.5
41.9
38.9
45.9
Overall
49.6
53.0
54.4
55.5
54.0
50.8
52.0
49.1
56.2
Simpler-Google
"Move 7up can near the opple"
<thinlo
The image shows 7up con, sponge, and on apple toble.
First, need locate the 7up and the
apple.... place
to 7up
opple. apple....
now right
</think
Simpler-Bridge
"ut eggplant into yellow basket"
<thinio
First, the aggplent lont in the sink. and basket
gripper will
eggplont,
corry
Okay. ready give final trajectory: move to eggplont, lift and place in basket.
</think
LIBERO-Long
Piikk the book and place it in the back comportm."
<thisio
Let's broak down the tosk by
identify book
compartment Use robot's orm near
right.
pick Move the book smoot from left right
Place it in the comportment....
</think
Figure Qualitative results of intermediate reasoning steps and visualized trajectory for robot manipulation tasks on SimplerEnv and LIBERO benchmarks.
Embodied Reasoning In Tab. 2 assess the reasoning capability of ThinkAct in embodied scenarios on three benchmarks:
Bench2 [35], RoboVQA (38), and OpenEQA (29 EgoPlan- Bench2 (35] measures multi-step planning egocentric daily-life scenarios, while RoboVQA (38)
focuses on long-horizon reasoning in robotic manipulation. ThinkAct outperforms the second-best method by 2.5% and 4.1 BLEU score on these two benchmarks, demonstrating its strength in long-horizon and multi-step planning. Separately. OpenEQA 2 measures zero-shot embodied understanding across diverse environments. The enhanced reasoning ability of Act enables better generalization and scene comprehension, resulting in strong performance on this benchmark.
4.3 Qualitative Results
Fig. we qualitatively showcase the reasoning process and execution scenes of two manipulation examples from the Simpler-Bridge (20) and LIBERO-Long (24) tasks. In the LIBERO-Lome task "Pick up the book and place it the back compartment," ThinkAct decomposes the instruction into sub-tasks: (1) pick up the book, (2) move from left to right, and (3) place it in the compart-

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Q: Is it possible to move the paper to left right now?
Q: What shape is the carpet on the floor?
ThinkAct
ThinkAct
LthinloLet me think about this for moment. In the video. I can see that the desk has a few items on it. On the right side, there are clready some pieces of poper stacked neatly, and near the front of the desk, notice some everyday objects maybe fissue box, a pen holder, and what looks like cup.
<thinloLet me think obout this for moment, The carpet on the floor stands out the image. It's quite lorge and has noticeable floral pattern.
Looking closely, the design seems centered, with blue tones domincting the color scheme. The overall look gives it a decorative and eleyont oppearance, Yes, it's a large blue carpet with a floral design:/thinke- <onswer-Thhe corpet on the floor is lorge and blue with floral pattern. < </onswer>
Woit, the scene feels bit cluttered, but not overly messy, The lighting also makes it easy to see the shadows of these abjects It sbems like typical workspace with multiple fools laid out for use.
Given the current setup, I'd say everything looks organized and ploced intentionally.f/ sailly.s//hiiie Thinlo aoovernoo//rswel
ThinkAct
<thinkoLet me think about for moment. image shows room with corpet on the floor. I'm trying to figure out Its shope.
ThinkAct <thinloLet me think about this for moment. The image shows a desk with various items on it, and a rebotic orm is holding piece of paper above the desk.
At first glance, the floral pattern the center draws attention, which might make the shope more complex, But looking closely of the edges, they straight and
The question is whether the papee con be moved to the left right now. Since the paper is already lifted off the surface, the orm has
right angles. The corners are clearly defined, and sides are parallel.
Full control of its movement.
So, despite the decorative design, the everall outline of the corpet restangular That seems clear now now.</thinle The carpet on the floor rectangular </onswer>
Given that, there's nothing preventing it from being moved left. So yes, it's bessible./thinle seodwrryyss/arsweee
(a) RoboVQA
(b) OpenEQA
Figure 4: Qualitative comparison of reasoning process and the derived answer for our ThinkAct with and without RL for embodied reasoning tasks Robo and OpenEQA benchmarks. Red denotes incorrect reasoning and answers, while green indicates correct ones.
ment, demonstrating its long-horizon planning capability. We also visualize the planned trajectory, confirming that the gripper closely follows the reasoning-guider plan during execution.
To better illustrate the impact of RL on the reasoning process, Fig. 4 compares ThinkAct before and after RL fine-tuning on embodied reasoning tasks. As we can observe in Fig. aa), using a RoboVQA [38) example, the SFT cold-start model focuses only on the current state and fails to reason over future steps. while the RL-tuned model successfully infers the correct answer. Also, as demonstrated in Fig. 4b, from OpenEQA [29], the cold-start model misinterprets the question, whereas the RL-tuned version demonstrates improved question and environment understanding. More qualitative comparisons and demo videos are provided in the supplementary material.
4.4 Ablation Study
In Tab. we ablate the proposed goal reward r geal and trajectory reward Fin to analyze their individual contributions reasoning and planning. We start from the full version of ThinkAct, which achieves the best performance across all benchmarks. Removing the trajectory reward leads to a noticcable drop, indicating that rinj is essential for learning coherent and structured planning behaviors. Without the goal reward, performance also declines, suggesting that Ygoul plays a key role in incentivizing long-horizon reasoning. When both Furaj and rgooal are removed, leaving only QA-style reward from QA datasets, the model shows only marginal improvements over the SFT baseline, confirming that action-aligned visual feedback is critical for effective multi-step planning in embodied settings. Finally, the SFT cold-start model without RL yields the lowest scores, verifying the effectiveness of our RL fine-tuning for eliciting the reasoning capability in MLLMs. More ablation studies (e.g.. the number of interactions per reasoning step N) are provided in the supplementary material.
4.5 Analysis of ThinkAct
In this section, we analyze the capabilities of ThinkAct in enhancing robotic manipulation by embodied reasoning. We focus on two key aspects: (1) how reasoning facilitates effective few-shot adaptation to new tasks and environments, and (2) how enables the robot to detect failures and perform self-correction during task execution. Through both quantitative experiments and qualitative
8

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OpenVLA
52.0
Magma
42.5
Table Quantitative ablation study for our proposed RL rewards in ThinkAct SimplerEnv, EgoPlan-- Bench2, and RoboVQA benchmarks.
ThinkAct
35.1
35.5
Success Rate
24.0
Method
SimplerEnv
EgoPlan
RoboVQA
ThinkAct (Ours)
60.1
48.2
59.8
14.0
Ours w/o
59.2
47.9
58.5
Ours w/o Tgaal
59.1
47.6
58.9
Ours w/o Tunjo Food
56.9
47.2
58.3
Spatial
Object
Goal
Average
SFT cold-start
56.4
46.4
57.9
Figure 5: Few-shot adaptation results on LIBERO. We use 10 demonstrations per task for fine-tuning.
Struggle to grasp Replan & Execute
<thinko First, the robot needs to move the mug cleser to the microwave This requires the microwave, which
(a)
next logical step. However, the gripper seems to be struggling to grasp the mug securely. This indicates that the previous grasp was successful, should reassess gripper's 's positioning and try to regrasp the mug before continuing Based on this reasoning, the next steps are:
move the mug up and
Reflection
& Self-Correction
Regrosp the mug securely.
2. Place the mug inside the microwave.
Close microwave door.
The next step should be reattempt grasping before proceeding to place the and close the door.</thinle
target objoct!
Replan Execulo
(b)
<thinlo The cream cheese box is clearly visible on the table, and
However, the video shows that the robot has successfully picked the cheese box
Reflection
placed it in the basket.
& Self-Correction
indicates that Therefore, the robot's 5 arm must now:
needs be picked up and placed basket again
toward cream cheese box.
2. The robot picks up the cream cheese box.
robat places
cheese the basket
</thinle
Figure 6: Demonstration of self-reflection and correction capability ThinkAct. The reasoning MLLM identifies the failure and generates revised plan that recovers from execution error.
examples, we demonstrate the unique advantages of leveraging reasoning MLLM to tackle embodied action tasks. We further provide the analysis of MLLM backbones in the supplementary material.
Reasoning Enhance Few-Shot Adaptation As we can observe Fig. and Fig. ThinkAct is capable of describing the environment and decomposing task instructions into meaningful sub- goals. To validate whether such reasoning improves the action model's adaptability, we conduct few-shot adaptation experiment on LIBERO benchmark (24.. Specifically, we use LIBERO- Spatial and LIBERO-Object to evaluate adaptation unseen environments, and LIBERO-Goo to test adaptation to new skills. We fine-tune the action model on just 10 demonstrations per task and evaluate performance over 100 trials. As shown Fig. 5 AhinkAct consistently outperforms state-of-the-art methods, achieving the highest success rates across all tasks. Notably, it surpasses Magma [48] by 7.3% on LIBERO-Goal and by 9.5% on LIBERO-Spatte -Satiaal, demonstrating the effectiveness of reasoning capability for few-shot generalization in both novel skills and environments.

---

Reasoning Elicit Reflection and Self-Correction Failure detection and self-correction are critical for robust robot manipulation 26]. To evaluate whether ThinkAct can reason about and recover from execution errors, we enable the reasoning MILM to observe more contextual information during execution by extending its input from single image or to short video segment Ot-N:t- This temporal context allows Act to detect failures, reconsider the situation, and replan accordingly. For example, as shown in Fig. ((a), the robot fails to grasp a mug. The reasoning MLLM identifies the issue, noting that the gripper is struggling, and suggests adjusting its position to reattempt the grasp. In Fig. (()), the robot attempts to move an object to a basket, but fails to pick it up in the first place. The MLLM detects the failure and replans the pickup, leading to successful completion. These cases highlight ThinkAct's 's ability to detect and recover from execution errors through reasoning.
Inference Speed We compare the inference speed ThinkAct with the end-to-end Open [T6] on LIBERO [24] tasks using A100 GPU. On average. ThinkAct takes 17% longer execution time than OLen primarily due to the autoregressive reasoning process. We note that while the inference time slightly increases, our embodied reasoning, significantly boosts downstream task performance. That is, ThinkAct outperforms OpenVLA on all four LIBERO task categories, achieving success rate improvements of 2.8% spatial, 3.2% on object, 8.4% on goal, and 15.3% on long- marghorizon tasks. These results show that the reasoning overhead is justified by significant performance gains, highlighting the effectiveness of embodied reasoning for robot manipulation.
test-time scaling paradigm,
5 Conclusion
We presented ThinkAct, a framework that reinforces visual latent planning for vision-language-actionn reasoning tasks. By combining action-aligned reinforcement learning with reasoning-enhaneee action adaptation, ThinkAct enables embodied agents to think before acting and execute robust actions in dynamic environments. Through extensive experiments across embodied reasoning and robot manipulation benchmarks, we demonstrated strong long- mon-hhorzzon planning, few-shot adaptation, and emergent behaviors such as failure detection and self-correction, providing a scalable path toward more deliberative and adaptable embodied AI systems.
Limitations Since ThinkAct builds on pretrained multimodal LLMs, it inevitably inherits their limitations, particularly hallucinations in visual spatial reasoning. This can lead generated plans that reference incorrect object attributes or spatial relationships, affecting downstream execution. While our latent planning and action grounding mitigate this some extent, future work on grounding- aware training or hallucination suppression in MLLMs may further improve robustness and reliability in real-world deployment. In addition, while we only include 2D traces calculate the reward signals, our reward framework can be readily extended to incorporate contact-rich signals into the total reward function (Eq. 3). The proposed aalignd visual reward allows extension with additional reward components that capture contact-rich dynamics. We will leave them for future research.
Broader Impacts Our work aims to enhance the reasoning capabilities of embodied agents, which could support real-world applications such as assistive robotics, home automation, and industrial systems. In particular, models like ThinkAct may help robots better interpret vague instructions and execute multi-step plans in dynamic environments. However, increased autonomy and reasoning ability in embodied systems also raise potential concerns. Misinterpretation of ambiguous commands, reliance on hallucinated visual reasoning, or overconfidence CoT outputs could result unintended behaviors, especially in safety-critical settings. Hence, future research on safeguards or alignment with human intent could further help mitigate these risks.
This work is supported in part by the National Science and Technology Council via grant NSTC 113-2634-F-002-000 NSTC 1 14--221-EE--- --0-0000000----- and NSTC 1 114-2640-E-002 006, and the financial supports from the Featured Area Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education 1109000 We also thank the National Center for High-performance Computing (NCHC) for providing computational and storage resources.
Acknowledgment
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction clearly state the contributions of ThinkAct, in- cluding dual-system reasoning-action modeling, reinforcement learning with action-aligned reward, and experimental validation across VLA tasks.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification:
We include discussion of limitations the main paper.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model sell-speciiiatiinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
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Reviews:
[{"review_id": "umj6kxCySF", "reviewer": "Reviewer_MRi2", "summary": "The paper introduces ThinkAct, a dual-system framework that integrates high-level reasoning with low-level action execution for vision-language-action (VLA) tasks. The key innovation lies in using reinforcement learning with action-aligned visual rewards to train a multimodal LLM to generate visual plan latents, which then guide a downstream action model. The approach is evaluated on simulated robot manipulation and embodied reasoning benchmarks, demonstrating improvements in long-horizon planning, few-shot adaptation, and self-correction.", "questions": "See Strengths And Weaknesses", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n- The separation of reasoning (via LLM) and action execution (via policy) is well-motivated and aligns with cognitive science principles of \"slow thinking\" and \"fast control.\"\n\n- The use of goal completion and trajectory consistency as reward signals is intuitive and empirically effective, particularly for manipulation tasks.\n\n- It outperforms prior methods (e.g., OpenVLA, CoT-VLA) on LIBERO and SimplerEnv, with notable gains in long-horizon tasks (e.g., LIBERO-Long).\n\nWeaknesses\n\n- The reward function (Eq. 1–3) relies on Euclidean distance and DTW metrics, which may not generalize to non-kinematic or abstract tasks (e.g., \"open the door\" requires contact-rich dynamics beyond 2D trajectory matching). The paper does not discuss extending rewards to such scenarios.\n\n- All experiments are conducted in simulation (SimplerEnv, LIBERO). While these benchmarks are standard, the lack of real-robot deployment raises concerns about sim-to-real gaps (e.g., visual discrepancies, control latency).\n\n- Results are averaged over only 3 seeds without standard deviations or confidence intervals (Table 1). This is insufficient for noisy robotics benchmarks, especially with high-variance tasks like \"Stack Blocks\" (8.7% success).", "was_revised": true, "final_justification": "The authors have addressed my concerns, I keep my rating.", "rebuttal": "## **Response to Reviewer MRi2**\n\n**(W1) The reward function (Eq. 1–3) relies on Euclidean distance and DTW metrics, which may not generalize to non-kinematic or abstract tasks (e.g., \"open the door\" requires contact-rich dynamics beyond 2D trajectory matching).**\n\nThanks to the reviewer for pointing this out. We agree that incorporating contact-rich signals (e.g., depth information or affordance maps) would be beneficial for tasks like \"open the door\" that require precise interaction dynamics beyond 2D trajectory matching.\nIt is worth noting, however, that we observe the reflection and self-correction capabilities learned from our reinforcement reasoning effectively alleviate such challenges. As shown in Figure 6 and A2, when objects cannot be grasped or are dropped during execution, ThinkAct detects the failure and replans to achieve success, resulting in SoTA performance on robot manipulation benchmarks, SimplerEnv (60.1%) and LIBERO (84.4%), which already include various contact-rich tasks.\n\nAdditionally, our reward framework can be readily extended to incorporate contact-rich signals into the total reward function (Eq. 3). The proposed action-aligned visual reward allows extension with additional reward components that capture contact-rich dynamics. We will include a discussion of incorporating contact-rich rewards for complex manipulation tasks in our revised version, and leave it for future research.\n\n\n**(W2) While the evaluation benchmarks are standard (i.e., SimplerEnv, LIBERO), the lack of real-robot deployment raises concerns about sim-to-real gaps (e.g., visual discrepancies, control latency).**\n\nThanks to the reviewer for raising this concern. As noted in [19], SimplerEnv has been shown to have performance highly correlated with real-robot deployment. We achieve state-of-the-art performance on these benchmarks across various visual variances (in Table 1) with only slightly increased inference latency compared to OpenVLA, as detailed in Sec. C.7 in the supplementary material.\n\nIn addition to simulation benchmarks, we have included comparisons on RoboVQA in Table 2, which is actually a real-robot benchmark for long-horizon reasoning tasks, where ThinkAct demonstrates superior performance with 59.8 BLEU score compared to existing methods.\n\nTo further demonstrate the real-world effectiveness of ThinkAct, we additionally conducted experiments on recently proposed benchmarks, including RoboFAC [A], a comprehensive benchmark for failure detection and correction on real-robot deployment, and RoboTwin2.0 [B], which specifically features its sim-to-real transfer characteristics for robot manipulation tasks. For the RoboTwin2.0 evaluation, we adopt RDT [C] as our baseline action model and demonstrate how ThinkAct enhances RDT's performance through our reasoning framework. As shown in the quantitative comparisons below, ThinkAct consistently outperforms existing methods, validating its potential for real-world deployment.\n\n**RoboFAC**\n|Method  | Failure locating | Task identification | Failure detection | Task planning | Failure identification | Failure explanation | High-level correction | Low-level correction | Average |\n|-------------------|------------------|----------------------|-------------------|----------------|-------------------------|----------------------|------------------------|-----------------------|---------|\n| Qwen2.5-VL        | 80.9             | 39.3                 | 83.4              | 35.6           | 36.0                    | 34.2                 | 44.7                   | 24.2                  | 47.3    |\n| **ThinkAct**      | **83.4**         | **53.9**             | **85.3**          | **48.7**       | **39.1**                | **51.3**             | **48.1**               | **28.3**              | **54.8** |\n\n**RoboTwin2.0**\n| Method     | adjust_bottle Easy | adjust_bottle Hard | beat_block_hammer Easy | beat_block_hammer Hard | lift_pot Easy | lift_pot Hard | Average |\n|------------|--------------------|---------------------|--------------------------|-------------------------|---------------|----------------|----------------|\n| RDT    | 85.6               | 77.2                | 80.4                     | 32.9                    | 81.6            | 19.5           | 62.9 |\n| **ThinkAct**   | **94.2**               | **89.6**                | **86.1**                     | **40.3**                    | **85.2**          | **22.2**           | **69.6** |\n\n[A] RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction. arXiv 2025.\n\n[B] RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation. arXiv 2025.\n\n[C] RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation. ICLR 2025.\n\n\n\n**(W3) Results are averaged over only 3 seeds without standard deviations or confidence intervals (Table 1). This is insufficient for noisy robotics benchmarks, especially with high-variance tasks like \"Stack Blocks\" (8.7% success).**\n\nThanks to the reviewer for this concern. As mentioned in L604, our evaluation protocol follows standard practices from OpenVLA and TraceVLA that report accuracy averaged over 3 seeds.\n\nWe would like to clarify that for tasks like \"Stack Blocks\" in SimplerEnv, each task already contains 24 different initial configurations, so the reported scores are actually averaged over 72 trials (3 seeds × 24 configurations). Similarly, each task in LIBERO contains 1500 evaluation trials (see L604), providing substantial statistical sampling. We will include the aforementioned details in our revision.\n\nTo further verify the statistical significance of our evaluation setup, we conducted additional experiments with 5 seeds and report the standard deviations in the table below. The means and standard deviations between the 3-seed and 5-seed setups are similar, confirming that our evaluation protocol provides reliable results:\n\n| Method                   | Simpler-Google (VM) | Simpler-Google (VA) | Simpler-Bridge (VM) | LIBERO     |\n|---------------------------|------------------------------|----------------------------|-----------------------------|-----------------|\n| OpenVLA               | 37.3                             | 43.1                          | 14.6                          | 76.5            |\n| Magma                   | 68.4                             | 62.6                         | 35.4                           | -                  |\n| ThinkAct (3 seeds) | 71.5 ± 2.6                   | 65.1 ± 3.4                 | 43.8 ± 5.0                  | 84.4 ± 1.7   |\n| ThinkAct (5 seeds) | 71.4 ± 1.9                   | 65.3 ± 2.5                 | 43.6 ± 3.7                  | 84.1 ± 1.9   |\n\nThis demonstrates that our reported improvements are statistically robust and not due to random variance."}, {"review_id": "NJWcxmKv4j", "reviewer": "Reviewer_K4qm", "summary": "In this work, the authors present ThinkAct, a framework that reinforces visual latent planning for vision-language-action reasoning tasks. ThinkAct trains a multimodal LLM to generate embodied reasoning plans guided by reinforcing action-aligned visual rewards. Experiments show strong long-horizon planning, few-shot adaptation, and emergent behaviors like failure detection and self-correction—advancing scalable, adaptable embodied AI.", "questions": "**Ablations.** While the quantitative ablation study is appreciated, Table 3's ablation results for rewards (r_traj, r_goal, and SFT cold-start) show only a modest performance drop of 2-5%. This limited drop raises questions about the true importance of the reasoning MLLM in this framework.\n\n**Table 2.**  I would like to clarify the distinction between Qwen2.5-VL* and ThinkAct. Does Qwen2.5-VL* still utilize the latent variable c_t, which is then passed to the Action Model? If not, it seems necessary to conduct an experiment comparing a high-level VLM that extracts c_t for a low-level controller (i.e., the Action Model).\n\n**The constructed prompt closely resembles the one used in RoboPrompt.** If this is accurate, please ensure proper citation within lines 225-235.\n\n**Reasoning Elicits Self-Correction.** To assess the importance of self-correction in robust robot manipulation, it would be beneficial to design experiments that explicitly demonstrate this capability. Such experiments would offer clearer insight into the consistency of self-corrective behavior, which is otherwise difficult to evaluate.", "limitations": "Overall, I liked this paper. However, I am worried about the importance of the GRPO Vs. a standard SFT.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1) In general, the paper is clear and the technical details are easy to understand.\n\n\n2) Exploring the reinforcement of visual latent planning for downstream reasoning tasks in VLAs is significant and important.\n\n\n3) The experiments section is thorough and extensive.", "was_revised": true, "final_justification": "After reading the authors' feedback and other reviewers' opinions, I would like to thank the authors for their rebuttal. \n\nMy questions have been fully addressed. Therefore, I raised my score to 5, as this paper presents a potentially exciting direction for future research.", "rebuttal": "## **Response to Reviewer K4qm**\n\n**(Q1) Why do Table 3's ablation results show only a modest performance improvement of 2-5% for rewards ($r_{traj}$, $r_{goal}$) from SFT cold-start? This limited improvement raises questions about the true importance of the reasoning MLLM in this framework.**\n\nWe would like to clarify that, following previous works [11,13], our reasoning framework includes both SFT cold-start and RL stages, where the SFT cold-start includes training on CoT data (i.e., Video-R1-CoT), as mentioned in Sec. B.2.2, making it already a reasoning-capable baseline. The modest improvements in Table 3 reflect the benefits of individual RL reward components ($r_{traj}$, $r_{goal}$) rather than the overall contribution of the reasoning framework itself.\n\nTo properly assess the importance of our proposed reasoning framework, we additionally conduct a non-reasoning SFT baseline (Qwen2.5-VL\\*\\*) that is trained without any CoT data, unlike SFT cold-start, which already includes reasoning capability. The comparison below shows the true impact of our reasoning framework:\n\n| Method                   | SimplerEnv (Avg.) | LIBERO | EgoPlan-Bench2 | RoboVQA | OpenEQA | Average |\n|---------------------------|-----------------------|-------------|-----------------------|-----------------|---------------|-------------|\n| Qwen2.5-VL\\*\\*        | 52.8                    | 75.2        | 44.9                    | 56.4            | 49.0          | 55.7       |\n| **ThinkAct**                 | **60.1**                    | **84.4**       | **48.2**                    | **59.8**            | **56.2**          | **61.7**       |\n| $\\Delta$                     | +7.3                    | +9.2        | +3.3                   | +3.4            | +7.2          | +6.0       |\n\nWe note that the qualitative examples in Figure 4 demonstrate that RL performs better in complex reasoning scenarios requiring visual grounding capability. Additionally, we evaluated our ThinkAct on the RoboFAC [A] benchmark that specifically requires reasoning about failure detection and correction. This benchmark further demonstrates that our RL reasoning significantly improves upon the SFT cold-start baseline, with substantial performance gains that highlight the critical role of reinforced reasoning in embodied tasks.\n\n|Method  | Failure locating | Task identification | Failure detection | Task planning | Failure identification | Failure explanation | High-level correction | Low-level correction | Average |\n|-------------------|------------------|----------------------|-------------------|----------------|-------------------------|----------------------|------------------------|-----------------------|---------|\n| SFT cold-start        | 80.6             | 43.3                 | 79.3              | 38.7           | 35.6                    | 43.8                 | 46.4                   | 25.6                  | 49.2    |\n| **ThinkAct**      | **83.4**         | **53.9**             | **85.3**          | **48.7**       | **39.1**                | **51.3**             | **48.1**               | **28.3**              | **54.8** |\n\n[A] RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction. arXiv 2025.\n\n\n\n**(Q2) Does Qwen2.5-VL\\* in Table 2 utilize the latent variable $c_t$ passed to the Action Model? If not, an experiment comparing a high-level VLM that extracts $c_t$ for the low-level controller seems necessary.**\n\nNo, Qwen2.5-VL\\* does not utilize the latent variable $c_t$ since it is a baseline that was fine-tuned on VQA datasets (i.e., EgoPlan-IT and RoboVQA) for embodied reasoning tasks in Table 2. For the evaluation of embodied reasoning benchmarks, only textual outputs for QA tasks are generated by the VLM part, with no visual latent $c_t$ and action model involved.\n\nAs suggested by the reviewer, we constructed an additional baseline (denoted as Qwen2.5-VL\\*\\*) for this comparison. As mentioned in the response to Q1, Qwen2.5-VL\\*\\* is trained from Qwen2.5-VL\\* on trajectory prediction datasets so that it can extract visual latent $c_t$ for a low-level controller. The comparisons below demonstrate that our action-aligned visual rewards for RL reasoning are crucial for effective latent planning:\n\n| Method                   | Simpler-Google (VM) | Simpler-Google (VA) | Simpler-Bridge (VM) | LIBERO | Average |\n|---------------------------|------------------------------|-----------------------------|-----------------------------|-------------|--------|\n| Qwen2.5-VL\\*\\*  | 65.3                             | 55.4                           | 37.7                          | 75.2        | 58.4 |\n| **ThinkAct**                 | **71.5**                             | **65.1**                           | **43.8**                          | **84.4**        |**66.2** |\n\nThis ablation confirms that simply extracting visual latents without our action-aligned reinforcement learning achieves significantly lower performance, validating the importance of our proposed reasoning framework.\n\n\n**(Q3) The constructed prompt closely resembles the one used in RoboPrompt. If this is accurate, please ensure proper citation within L225-235.**\n\nWe thank the reviewer for the suggestion. As noted in Supplementary Table 1, our prompt design is referenced from Video-R1 [11], encouraging the model to reason through the scene and generate a 2D trajectory as a visual plan. In contrast, RoboPrompt’s prompt [D] is designed to guide LLMs to directly predict robot actions through in-context learning examples without reasoning steps. We are happy to include a discussion of RoboPrompt in the Related Works section in the revised version for completeness.\n\n[D] In-context learning enables robot action prediction in LLMs. ICRA 2025.\n\n\n**(Q4) To assess the importance of self-correction in robust robot manipulation, experiments (e.g., quantitative evaluation) that explicitly demonstrate this capability would be beneficial.**\n\nThanks to the reviewer for this suggestion. In addition to the qualitative examples shown in Figure 6 and Figure A2 demonstrating the reflection and self-correction capability, we additionally evaluated on the recently proposed RoboFAC [A] benchmark, which is specifically designed to assess failure understanding and correction in robot manipulation through a QA format. As shown in the table below, we compare our ThinkAct with the non-reasoning baseline (Qwen2.5-VL), demonstrating the effectiveness of our proposed ThinkAct for self-correction capability:\n\n|Method  | Failure locating | Task identification | Failure detection | Task planning | Failure identification | Failure explanation | High-level correction | Low-level correction | Average |\n|-------------------|------------------|----------------------|-------------------|----------------|-------------------------|----------------------|------------------------|-----------------------|---------|\n| Qwen2.5-VL        | 80.9             | 39.3                 | 83.4              | 35.6           | 36.0                    | 34.2                 | 44.7                   | 24.2                  | 47.3    |\n| **ThinkAct**      | **83.4**         | **53.9**             | **85.3**          | **48.7**       | **39.1**                | **51.3**             | **48.1**               | **28.3**              | **54.8** |\n\nThe results clearly indicate that our RL-enhanced reasoning framework significantly improves failure detection and correction abilities compared to standard VLMs, providing quantitative evidence for the self-correction capabilities observed qualitatively in our manipulation experiments (Figure 6 and Figure A2)."}, {"review_id": "G9pZCjvJHr", "reviewer": "Reviewer_h8uF", "summary": "The paper proposes ThinkAct, which try to bridge high-level reasoning with low-level action execution via reinforced visual latent planning. The method contains a SFT part, a RL tuning part and a final fine-tuning part. The author evaluate the results on several simulators and QA tasks to show the efficiency of the method.", "questions": "1. More explanation about the intuition on the design of the goal reward would be helpful. For goal completion, why would the goal reward contains a comparison on the start point $f(p_1, \\hat{p}_1)$? \n\n2. In L146-150, the authors propose to use QA-style accuracy reward. Can you provide more details about this reward?\n\n3. For OXE dataset used in the cold-start phase, what are the latent embeddings $v$ for OXE dataset?\n\n4. For the experiment settings, the comparison needs to be conduct in a fair setting. The authors propose to use a subset of OXE during SFT cold-start, RL training and finetuning (100K samples). What is the detailed mixture of this subset? Are these three stages using the same data? For baselines like OpenVLA, Magma, etc., they seem to use a mixed data that is of various source and evaluate in a zero-shot fashion. If these baselines are only pretrained on a wider range of OXE (for instance) data , the performance will be worse naturally. To make a fair comparison, the author should finetune the baselines using the same subset which seems to contain more test relevant data.\n\n5. The main motivation of the paper is to build a system that can bridge high-level reasoning and low-level action. However, the evaluation tasks (Sipmler and LIBERO) mostly contain simple tasks like pick and place that does not require high level planning. This may hinder the proof of the final motivation.\n\n6. Misc.: It would be helpful to include the definition of $r_{format}$ (eq.3) to make the paper self-contained.", "limitations": "N/A", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- The writing is mostly clear and easy to follow.\n\n- The evaluation especially the few-shot adaptation part seems to be interesting.\n\nWeakness:\n\n- The method itself seems to be a combination of two existing methods (Megma and GRPO) which limited the novelty.\n\n- The author claims the system can bridge high level reasoning and low level actions. However, most evaluation tasks are pick-and-place and do not require serious high level reasoning.\n\n- Some experiment settings seem to be misaligned with baselines. Need additional experiments / explanation to help clarify the source of improvements comes from the method rather than the different data mixture which might contain more test-relevant dataset. See questions below.", "was_revised": true, "final_justification": "thank the authors for the response! I think the authors find an interesting way to combine high level reasoning and robot execution and the combination is not naive. However, my major concerns still hold:\n\n(1) the major claim is to \"bridge high-level reasoning with low-level action execution via reinforced visual latent planning\" However, the current experiments fail to evaluate the method on tasks where high-level reasoning is crucial. The existing benchmarks can be solved more effectively with other methods that do not rely on high-level reasoning.\n\n(2) From the previous response, the authors seem to\n\n(A) use high quality and test-related data (fractal20220817_data and bridge) for SFT cold-start and RL training according to previous response.\n(B) use 100K subset with similar mixture as OXE for the action model.\nWhat I consider bring extra advantages for the proposed method is (A) because we all know that for current embodied policy, training on test related embodiment/tasks will increase the final performance. In the authors response, the authors mainly focus on fine-tuning baselines using (B) and explain the data format for (B) which make the concern still hold.", "rebuttal": "## **Response to Reviewer h8uF**\n\n**(W1) The method itself seems to be a combination of two existing methods (Magma [44] and GRPO [36]) which limited the novelty.**\n\nThanks to the reviewer for pointing this out. For Magma, it uses trajectory prediction merely as an auxiliary training task, and it directly outputs robot actions during inference. For our work, we exploit visual trajectory latents as a long-term visual plan, that guides and enhances the action model for long-horizon robot manipulation tasks. Regarding GRPO, it is simply the RL optimization technique we adopt (commonly used across many works [11,25,39]). In other words, our innovation lies in **action-aligned visual rewards** that combine goal completion and trajectory consistency, enabling **embodied reasoning** grounded in visual-spatial dynamic scenes while unleashing capabilities like **reflection** and **self-correction** (Figure 6). A naive combination of Magma with GRPO cannot achieve reflection and self-correction due to the lack of our dual-system architecture that connects structured reasoning with executable actions through visual latent planning.\n\n\n**(W2, Q5) The main motivation of the paper is to build a system that can bridge high-level reasoning and low-level action. However, the evaluation tasks (Sipmler and LIBERO) mostly contain simple tasks like pick and place that does not require high level planning. This may hinder the proof of the final motivation.**\n\nThanks to the reviewer for raising this concern. We would like to clarify that SimplerEnv and LIBERO do not just contain simple tasks like pick and place. For example, LIBERO-Long contains tasks such as \"turn on the stove and put the frying pan on it\" or \"stack the right bowl on the left bowl and place them in the tray\", which require high-level planning beyond primitive pick-and-place actions and multi-step sequential reasoning.\n\nTo further demonstrate our advantage on complex reasoning tasks, we additionally conducted comparisons on the recently proposed RoboTwin2.0 [B] benchmark, which contains tasks that require reasoning about object affordances and tool use to complete complex manipulation scenarios (e.g., “beat block with hammer”). Using RDT [C] as our baseline action model, ThinkAct can further enhance RDT's performance on tasks requiring high-level planning, as shown in the table below:\n\n| Method     | adjust_bottle Easy | adjust_bottle Hard | beat_block_hammer Easy | beat_block_hammer Hard | lift_pot Easy | lift_pot Hard | Average |\n|------------|--------------------|---------------------|--------------------------|-------------------------|---------------|----------------|----------------|\n| RDT    | 85.6               | 77.2                | 80.4                     | 32.9                    | 81.6            | 19.5           | 62.9 |\n| **ThinkAct**   | **94.2**               | **89.6**                | **86.1**                     | **40.3**                    | **85.2**          | **22.2**           | **69.6** |\n\n[B] RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation. arXiv 2025.\n\n[C] RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation. ICLR 2025.\n\n\n**(W3, Q4) The comparison seems to not be conducted in a fair setting. ThinkAct uses a subset of OXE during SFT cold-start, RL training, and fine-tuning (100K samples). What is the detailed mixture of this subset? Are these three stages using the same data? Baselines like OpenVLA and Magma seem to be pretrained only on a wider range of OXE data and are evaluated zero-shot. To make a fair comparison, the authors should fine-tune the baselines using the same subset, which seems to contain more test-relevant data.**\n\nThanks to the reviewer for raising this important concern about experimental fairness. We would like to clarify that the comparison setting with OpenVLA and Magma is actually fair in our current setup.\n\nTo clarify our data usage across the three training stages, as detailed in Sec. B.2.1, SFT cold-start and RL training use fractal20220817_data and bridge subsets from OXE, which are selected because of their high-quality robot trajectories, and we do not require their action ground truth for these two stages. The reasoning-enhanced action adaptation stage uses 100K data points sampled from the complete OXE dataset to connect the visual latent with the action model. We note that, both OpenVLA and Magma are trained on the complete OXE dataset, which means all methods (including ours) are trained on overlapping data, ensuring a fair comparison.\n\nAs suggested by the reviewer, we are happy to further conduct comparisons by fine-tuning both OpenVLA and Magma on the same 100K samples used for our reasoning-enhanced action adaptation. As shown below, ThinkAct continues to outperform both baselines even under identical fine-tuning data conditions:\n\n| Method            | Simpler-Google (VM) | Simpler-Google (VA) | Simpler-Bridge (VM) | Average |\n|---------------------|------------------------------|-----------------------------|-----------------------------|-------------|\n| OpenVLA         |         37.3                    |     43.1                      |         14.6                   | 31.7        |\n| OpenVLA (FT) |         37.7                    |     40.4                      |         16.5                   |  31.5       |\n| Magma            |         68.4                    |      62.6                     |         35.4                    | 55.5       |\n| Magma (FT)     |         70.9                   |      61.7                     |         39.2                   | 57.3        |\n| **ThinkAct**           |         **71.5**                   |       **65.1**                    |         **43.8**                   | **60.1**        |\n\n\n**(Q1) For goal completion, why would the goal reward contains a comparison on the start point?**\n\nThanks to the reviewer for this question. While it would be straightforward to design a goal reward focusing only on the final position, we include the start point in our design to ensure that the reasoning model accurately identifies the initial state and the gripper location before planning the manipulation sequence.\n\nThis design helps the model better understand both where the task begins and where it should end, encouraging the generation of visually grounded reasoning that connects initial observations with goal states. In combination with the trajectory reward, this supports the generation of coherent and executable plans that are properly anchored to the current scene configuration rather than producing abstract reasoning disconnected from the visual context.\n\n\n**(Q2) In L146-150, the authors propose to use QA-style accuracy reward. Can you provide more details about this reward?**\n\nThanks to the reviewer for requesting these details. Following [11, 39], the QA-style accuracy reward is computed by either answer accuracy for multiple-choice QA tasks (e.g., Reflect, EgoPlan, and LLaVA-Video-178K) or averaged ROUGE-1/2/L scores for open-ended QA tasks (e.g., RoboVQA and LLaVA-Video-178K), as mentioned in Supplementary Sec. B.1.1.\n\nOnce we obtain the QA reward $r_{\\text{QA}}$, we use the same approach as in Eq. 3 that combines the QA-style reward with the format reward, and then optimize using GRPO. Specifically, for QA tasks, the total reward becomes: $r = 0.9r_{\\text{QA}} + 0.1r_{\\text{format}}$. We will add these details in the revised version to clarify the reward computation for different task types.\n\n\n**(Q3) For OXE dataset used in the cold-start phase, what are the latent embeddings $v$ for OXE dataset?**\n\nThanks to the reviewer for this clarification request. We would like to clarify that we do not have reasoning annotations for the OXE dataset, so during the SFT cold-start phase, we directly predict trajectories without generating reasoning latent embeddings $v_t$ for OXE data.\n\nAs mentioned in Sec. B.2.2, during SFT cold-start, the reasoning latent embeddings $v_t$ are only generated for datasets that contain reasoning annotations (i.e., Video-R1-CoT). For OXE trajectory data, the model learns to map visual observations and instructions directly to trajectory predictions, as the aim is to build foundational trajectory prediction capability before reinforcement learning.\n\n\n**(Q6) Misc.: It would be helpful to include the definition of $r_\\text{format}$ (Eq. 3) to make the paper self-contained.**\n\nIn our implementation, the format reward $r_\\text{format}$ is set to 1 when the model's response adheres to the required output structure, specifically the \"\\<think\\> … \\</think\\> \\<answer\\> … \\</answer\\>\" format, and 0 otherwise. This follows the conventions used in DeepSeek-R1 [13] and Video-R1 [11], as also described in Supplementary Sec. B.2.2. We will include the explicit definition of $r_\\text{format}$ in the revised version to make the paper self-contained."}, {"review_id": "CRGBkTTBFh", "reviewer": "Reviewer_nnRq", "summary": "The paper introduces a new VLA model called \"ThinkAct\", in which a multimodal LLM generates embodied reasoning plans expressed as language and visual latents, and a subsequent action model generates low level actions. Differently from other work, which just made use of Chain of Thought to boost a similar type of VLA, ThinkAct uses Reinforcement Learning (specifically GRPO) to optimize the first planning step.\n\nThe approach is evaluated on simulated manipulation benchmarks (SimplerEnv and LIBERO), embodied reasoning benchmarks, and is also investigated qualitatively. Overall, the approach improves performance and induces better reasoning traces over embodied actions.", "questions": "* Was the \"SFT cold-start\" ablation benchmarked in a more granular fashion for manipulation and embodied reasoning? How does it fare against the other benchmarked methods?\n\n* Is it possible to see a benchmark of ThinkAct against \"SFT cold-start\" with comparable compute budgets (running the SFT model training for longer)?", "limitations": "Limitations are discussed in the Appendix, not the main paper.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\nThe paper proposes an interesting architecture and training method for VLAs, applying the recent advances in RL-based reasoning models to robotics. The overall architecture is in line with the state of the art (such as in using a two-stage approach to separate planning and action, and in using a DiT policy for action generation), making the presented approach a meaningful investigation of the impact of RL-based planning when compared to the state of the art.\n\nThe choice of visual plan latents is interesting and might be the key behind improved sample efficiency of the method compared to other baselines.\n\n**Weaknesses**\n\nThe main criticism of the work is that it appears that the impact of the RL finetuning technique used for enhancing planning is not completely disentangled from the overall \"visual plan latent\"-based proposed architecture for the VLA itself. When comparing the ThinkAct approach against others on manipulation tasks (Table 1), it seems to often severely improve over alternatives. However, in the ablation test (Table 3), it appears that the introduction of the RL step only adds a few percentage points of performance compared to \"SFT cold-start\", which consists of the proposed architecture, without the RL fine-tuning part.\n\nIt seems that the proposed architecture might be overall contributing to much of the improvement in performance: it would have been interesting to see \"SFT cold-start\" benchmarked with more details in the main experiment tables. In addition: in the ablation, ThinkAct and \"SFT cold-start\" are also not strictly comparable in compute budget. It would have been interesting to compare ThinkAct against a version of \"SFT cold-start\" that was trained for longer in order to match the total ThinkAct compute budget.", "was_revised": true, "final_justification": "The authors have addressed my comments and I thus update my score to a 5. There are still some weaknesses overall, plus the performance gains remain marginal, but the overall introduction of RL for reasoning in robotics is interesting and worthwile, so I recommend acceptance.", "rebuttal": "## **Response to Reviewer nnRq**\n\n**(W1) While ThinkAct shows significant improvements against others in Table 1, the ablation study (Table 3) shows that RL fine-tuning only adds a few percentage points compared to \"SFT cold-start.\" It seems that the major performance gains come from the architectural design rather than the proposed RL method.**\n\nWe appreciate the reviewer raising this important concern about disentangling the contributions of our architecture versus the RL fine-tuning technique. We would like to clarify that, following previous works [13,11], our reasoning framework includes both SFT cold-start and RL stages, where the SFT cold-start already incorporates reasoning capabilities with CoT data (i.e., Video-R1-CoT), as mentioned in Sec. B.2.2. Therefore, the comparison in Table 3 reflects the improvement from RL over an already reasoning-capable baseline, not the overall impact of reasoning itself.\n\nTo properly disentangle these contributions, we conducted experiments with a non-reasoning baseline (Qwen2.5-VL\\*\\*) that uses the same \"visual plan latent\"-based architecture but is trained without any CoT data, as shown in the table below:\n\n| Method                   | Simpler-Google (VM) | Simpler-Google (VA) | Simpler-Bridge (VM) | LIBERO | Average |\n|---------------------------|------------------------------|-----------------------------|-----------------------------|-------------|-------------|\n| Qwen2.5-VL\\*\\*      | 65.3                             | 55.4                           | 37.7                          | 75.2        | 58.4 |\n| **ThinkAct**                 | **71.5**                             | **65.1**                           | **43.8**                          | **84.4**       | **66.2** |\n| $\\Delta$                      | +6.2                            | +9.7                          | +6.1                          | +9.2        | +7.8\n\nAdditionally, we evaluated our ThinkAct on the RoboFAC [A] benchmark that specifically requires reasoning about failure detection and correction, further demonstrating that our RL reasoning significantly improves upon both the SFT cold-start and non-reasoning baselines. This confirms that both the reasoning capabilities and RL fine-tuning are essential components, with the qualitative results in Figure 4 demonstrating our action-aligned visual rewards result in visually grounded reasoning thoughts.\n\n|Method  | Failure locating | Task identification | Failure detection | Task planning | Failure identification | Failure explanation | High-level correction | Low-level correction | Average |\n|-------------------|------------------|----------------------|-------------------|----------------|-------------------------|----------------------|------------------------|-----------------------|---------|\n| SFT cold-start        | 80.6             | 43.3                 | 79.3              | 38.7           | 35.6                    | 43.8                 | 46.4                   | 25.6                  | 49.2    |\n| **ThinkAct**      | **83.4**         | **53.9**             | **85.3**          | **48.7**       | **39.1**                | **51.3**             | **48.1**               | **28.3**              | **54.8** |\n\n[A] RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction. arXiv 2025.\n\n\n**(W2, Q1) It seems that the proposed architecture might be overall contributing to much of the improvement in performance: it would have been interesting to see \"SFT cold-start\" benchmarked with more details in the main experiment tables.**\n\nWe appreciate the reviewer's suggestion for more detailed benchmarking of the SFT cold-start baseline. We conducted granular evaluations of our SFT cold-start across both manipulation and embodied reasoning benchmarks, comparing it against other methods as shown in the table below:\n\n\n| Method            | Simpler-Google (VM) | Simpler-Google (VA) | Simpler-Bridge (VM) | LIBERO |\n|------------------|----------------------|-----------------------|-----------------------|--------|\n| OpenVLA [15]          | 37.3                 | 43.1                  | 14.6                  | 76.5   |\n| CoT-VLA [49]          | -                    | -                     | -                     | 83.9   |\n| Magma [44]            | 68.4                 | 62.6                  | 35.4                  | -      |\n| SFT cold-start   | 67.4                 | 61.7                  | 40.2                  | 79.1   |\n| **ThinkAct**     | **71.5**             | **65.1**              | **43.8**              | **84.4** |\n\n| Method            | EgoPlan-Bench2 | RoboVQA | OpenEQA |\n|-------------------|----------------|---------|---------|\n| GPT-4V [1]            | 32.6           | 26.8    | 49.6    |\n| Qwen2.5-VL [2]        | 29.1           | 39.7    | 50.8    |\n| Magma [44]             | 29.8           | 31.2    | 49.1    |\n| SFT cold-start    | 46.4           | 57.9    | 53.3    |\n| **ThinkAct**   | **48.2**           | **59.8**    | **56.2**    |\n\nThe results demonstrate that our SFT cold-start already achieves competitive performance on manipulation tasks and strong results on embodied reasoning benchmarks (outperforming SoTAs like [1,2,44]). However, the full ThinkAct with RL fine-tuning consistently outperforms the SFT cold-start across all tasks, particularly excelling in complex scenarios requiring visual grounding and long-horizon planning.\n\nThis granular analysis highlights the crucial importance of our action-aligned visual rewards and RL fine-tuning for achieving optimal performance in embodied AI tasks, demonstrating that both architectural design and reward-guided reasoning optimization are essential components of our ThinkAct.\n\n\n**(W3, Q2) Is it possible to see a benchmark of ThinkAct against \"SFT cold-start\" with comparable compute budgets (running the SFT model training for longer)?**\n\nWe conducted additional experiments where we extended the SFT cold-start training from 20K to 26K iterations (matching the total compute budget of ThinkAct's 20K SFT + 6K RL iterations) as shown in the table below:\n\n| Method                  | Total Iter. | SimplerEnv | LIBERO | EgoPlan-Bench2 | RoboVQA | OpenEQA |\n|-------------------------|-------------|------------|--------|----------------|---------|---------|\n| SFT cold-start          | 20K         | 56.4       | 79.1   | 46.4           | 57.9    | 53.3    |\n| SFT cold start$^{†}$    | 26K         | 55.9       | 79.3   | 46.3           | 58.2    | 52.0    |\n| **ThinkAct**            | 26K         | **60.1**   | **84.4** | **48.2**     | **59.8** | **56.2** |\n\nHowever, we observed no significant improvement in performance across benchmarks when simply extending SFT training duration. This is because for robot manipulation and embodied reasoning tasks, it is crucial to ground the reasoning into the visual scenes. Simply increasing supervised training cannot achieve this grounding effectively, which is why our proposed action-aligned visual rewards are crucial for connecting reasoning with executable actions in visual environments."}]

Author rebuttal:

Meta-review: The paper proposes a framework that links reasoning and action execution via reinforced visual latent planning for embodied AI. The main strengths highlighted across reviews are the strong motivation, clear presentation, and extensive experiments demonstrating improved long-horizon planning, self-correction, and few-shot adaptation. Reviewers agreed that the idea of separating reasoning (via LLM) and execution (via action model) is well motivated and that the use of action-aligned visual rewards is intuitive and effective. A recurring weakness, however, was that the evaluation did not always convincingly demonstrate high-level reasoning beyond pick-and-place tasks, and some reviewers were concerned about fairness in the experimental setup and the reliance on potentially test-related data. One reviewer also emphasized that the novelty could be overstated, given overlaps with prior methods like Magma and GRPO, and questioned whether the improvements mainly came from architectural design rather than the RL step.

During rebuttal and discussion, the authors provided substantial clarifications and new results. They extended evaluation to harder tasks, which involve tool use, affordance reasoning, and failure correction, showing consistent gains. They also clarified the fairness of their data usage and added comparisons where baselines were fine-tuned on the same subsets, with ThinkAct still outperforming them. Reviewers who were initially skeptical (e.g., about the role of RL) acknowledged that the new experiments showed clear benefits beyond SFT cold-start, and one raised their score after rebuttal. While one reviewer’s concern about fully validating high-level reasoning remained partly unresolved, the majority view was that the authors sufficiently addressed the main criticisms and the contribution is valuable to the embodied AI community. Overall, given the well-motivated method, thorough experimental clarifications, and demonstrated improvements across multiple benchmarks, I recommend acceptance.
Decision: Accept (poster)