From Observations to Events: Event-Aware World Models for Reinforcement Learning
Authors: Zhao-Han Peng
Shaohui Li
Zhi Li
Shulan Ruan
Yu LIU
You He
Venue: ICLR 2026 Poster (2026)
Keywords: model-based reinforcement learning
online learning
reinforcement learning
Primary area: reinforcement learning
TL;DR: EAWM is an event-aware world model that automatically generates and segments events from raw observations to make model-based RL more robust and generalizable, achieving 10–45% gains and setting new state-of-the-art results across diverse benchmarks.

Abstract: While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive science perspective, humans segment continuous sensory streams into discrete events and rely on these key events for decision-making. Motivated by this principle, we propose the Event-Aware World Model (EAWM), a general framework that learns event-aware representations to streamline policy learning without requiring handcrafted labels. EAWM employs an automated event generator to derive events from raw observations and introduces a Generic Event Segmentor (GES) to identify event boundaries, which mark the start and end time of event segments. Through event prediction, the representation space is shaped to capture meaningful spatio-temporal transitions. Beyond this, we present a unified formulation of seemingly distinct world model architectures and show the broad applicability of our methods. Experiments on Atari 100K, Craftax 1M, and DeepMind Control 500K, DMC-GB2 500K demonstrate that EAWM consistently boosts the performance of strong MBRL baselines by 10\%–45\%, setting new state-of-the-art results across benchmarks. Our code is released at [https://github.com/MarquisDarwin/EAWM](https://github.com/MarquisDarwin/EAWM).

Published as a conference paper at ICLR 2026
FROM OBSERVATIONS TO EVENTS: EVENT-AWARE WORLD MODEL FOR REINFORCEMENT LEARNING
Zhao-Han Shaohui LiP, Zhi Li', Shulan Ruan', Yu Liu"; You He
P
Shenzhen International Graduate School, Tsinghua University 2College of Information Science and Electronic Engineering, Zhejiang University 3 Department of Electronic Engineering, Tsinghua University
ABSTRACT
While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to general- ize across structurally similar scenes and remain vulnerable to spurious variations such as textures color shifts. From a cognitive science perspective, humans seg- ment continuous sensory streams into discrete events and rely on these key events for decision-mmking Motivated by this principle, propose the Event-Aware World Model FEAWM), a general framework that learns event-aware representa- tions streamline policy learning without requiring handcrafted labels. FAWW employs an automated event generator to derive events from raw observations and introduces a Generic Event Segmentor (GES) identify event boundaries, which mark the start and end time of event segments. Through event prediction, the representation space shaped capture meaningful spatio-temport transitions. Beyond this, we present a unified formulation of seemingly distinct world model architectures and show the broad applicability of our methods. Experiments on Atari 100K, Craftax 1M, and DeepMind Control 500K, DMC-GB2 500K demon- strate that EAWM consistently boosts the performance of strong MBRL baselines by 10% -0%%,55% setting new state-of ohe art results across benchmarks. Our code is released at httpgggghhh..oo/o/ruuuuuuaaaaaae
INTRODUCTION
Historically. policy learning in complex environments within model-free reinforcement learning requires millions of interactions with environments (Jaderberg et al., 2017), which limits its real-world applications. MBRL algorithms utilize world models to capture the dynamics of the environment (Ha & Schmidhuber, 2018), learn representations from nigh-dimensional observations (Ehert et al., 2018; Hafner et al., 2019b; Zhang et al., 2019), and imagine future frames (Hafner et al., 2019a; Kaiser et al., 2020). Once the world model is established, the policy can be optimized on imagined trajectories, reducing reliance on real-world interaction and thereby improving sample efficiency.
Existing MBRL approaches focus on learning world models that predict future states through self- supervised learning in the observation space. As notable example. DreamerV3 (Hafner et al., 2025) estimates distributions over prior states (before observations) and posterior states (after observations) handle stochasticity. Transformer world models like TWISTER (Burchi & Timofte, 2025), RetNet- based (Sun et al., 2023) models like Simulus (Cohen et al., 2025), and diffusion models (Alonso et al., 2024) were exploited generate precise future observations. However, the training objectives in the observation space result in inaccuracy of long-horizon prediction (Li et al., 2025) and redundant information for policy learning. Moreover, predicting raw observations in stochastic environments is inherently ill-posed: for example, one cannot predict the outcome of a coin flip before it occurs.
In contrast, biological systems treat events as fundamental units of perception and action (Wahlheim Zacks, 2025). Inherent advantages of processing events include lower power consumption and lower latency, and theoretical optimality for dicision-making (Butz et al., 2021). Recent advances (González- Rueda et al., 2024; Lu et al., 2024) in neurobiology have revealed that the superior colliculus (SC)
"Corresponding Yu Liu uyu thu@mai . ttsinggua,. edu. en).

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Published as a conference paper at ICLR 2026
Ground Truth
Imagined events Differencs images Imag lned Images
Xime
Figure 1. Imagined frames and event predictions under the framework of EAWM at imagination step 9. The first two rows show ground-truth and imagined images, while the third row highlights their pixel-wise differences. Each column corresponds to a distinct trajectory. Notably, although the imagined frames may deviate from the ground truth in object positions, the event predictor consistently localizes spatial boundaries accurately.
contains neurons that respond exclusively to changes in visual scenes (c.g., looming predators or moving targets), enabling rapid behavioral responses. In addition, SC neurons align sensory inputs with motor commands in vector space, not just spatial coordinates. This allows real-time interception of moving objects by mapping dynamic sensory features like velocity to action trajectories (Hunt et al., 2025). These findings suggest that animals often respond directly to dynamic events rather than relying solely static observations. Consider, for instance, scenario where a large, unfamiliar creature rapidly approaches in a forest: a human would instinctively flee without waiting to fully identify the creature. In contrast, current world models, driven primarily by observation reconstruction. are prone to failure in such scenarios, particularly when faced with novel objects or dynamics absent from the training data. They put too much emphasis observation predictions, leading to poor generalization and ineffective policy learning. Nevertheless, humans' brains only make predictions of events rather than predictions of future observations (Ben-Yakov & Henson, 2018). This highlights the need for building world models that capture kinetic features for robust agent control- direction that has been largely neglected in prior MBRL research.
To that end, we present a unified framework called Event-Aware World Model (EAWM), which intro- duces generally applicable method that learns compact kinetic features from raw observations in the environment. Yet how SC neurons process kinetic features remains to be explored. We argue that event prediction is an effective way construct an event-aware agent within a computational frame- work, instead of building complicated system to filter out noise and take events inputs (Gehrig Scaramuzza, 2024). Because event prediction intrinsically constrains the representation space to meaningful apatio-temporal transitions through information bottleneck optimization. As illustrated in Figure 1, we find that event prediction is inherently less complex than observation prediction, since it abstracts away redundant information or low-level variation. We conduct experiments and demonstrate the strong performance and adaptability of EAWM for diverse scenarios, as shown in Figure 2. We observe that EAWM enhances the performance of base world models by a large margin in tasks requiring event-aware reactions, for example, with improvements of 55% in Breakowt and 11 115% in Acrobot Swingup. The main contributions of this work are summarized follows:
To the best of our knowledge, this paper, for the first time, systematically analyzes the advantages of kinetic features for policy learning and introduces a general framework to learn concise latent representations of events without requiring any manual labels.
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Published as a conference paper at ICLR 2026
EASimulustcura):
DIAMOND
400
Harmoey Denom
EADream/curs)
Stasala
CTRL
Dresmu111
EDD
DreamerV's
(a) Atari 100K
(b) Craftax 1M
(c) DeepMind Control 500K
(d) DMC-GB2 500K
Figure 2. Overview. RAWM surpasses existing model-free and model-based RI across 55 test tasks, encompassing both continuous and discrete control, as well as multi-modal observations. (a) Mean nurma-normalized scores and the 95% stratified bootstrap confidence intervals (Agarwal et al., 2021) on the 26 tasks of Atari 100K. (b) Percentage of scores against the maximum score in Craftax. (c) Mean returns on 10 challenging tasks from DeepMind Control Suite. (d) Mean returns over 6 tasks on 3 test environments from DMC-GB2.
We design generic event segmentor (GES) to identify event boundaries, which enable robust representation learning responsive to the critical events for multimodal observations.
We show the broad applicability of our methods through a unified formulation of seemingly distinet world model architectures, demonstrated by implementations of EADream and EASimulus.
With minimal tuning, show that our methods improve the baseline world models by 10%-45% setting new records across diverse established MBRL benchmarks.
2 BACKGROUND
2.1 REINFORCEMENT LEARNING
Reinforcement learning in the real world can be formalized as a Partially Observable Markov Decision Process (POMPP) (Kaelbling al., 1998) with observations o, E Ω2 actions a, E A, rewards r, E R, states S, S, and discount factor 7 E (0, 1]. An agent takes action according to the policy T-|1os,, a, which is a mapping from the history of past observations and actions to a probability distribution actions to take. The object is learn policy that maximizes the expected value of 00
accumulated discounted reward E, /
t= 7r SSu
s).
2.2 EVENT DEFINITION
Events are fundamentally defined as a segment of time at given location that perceived by an observer to possess a distinct commencement and termination (Zacks & Tversky, 2001). Extensive research has established that events are encoded, perceived, and cognitively processed in the human brain (Wahlheim & Zacks, 2025).
Visual Inputs. Within visual sensing, an event constitutes a localized variation in the logarithm of brightness (Gallego et al., 2020). Herein, the brightness Te(1;, ys) is proportional to the irradiance hitting the pixel (zt, 91) at the 2D camera plane at temporal point t. Let LG (z 31) log 4 v:). An event e,_ (Ix,, 34. t, p.) is triggered at pixel (z4, 34) when the magnitude of change attains predetermined temporal contrast threshold (Chakravarthi et al., 2024), denoted C₁:
if L, (Z,, 91) L (Zt, 81) C₁ P 1, if (x,, 9i) L. 3:) < -C
0,
otherwise
pi( 0) denotes the existence of events, and the sign of the brightness change denotes the direction of the event. Consequently, events can be derived algorithmically via established computational frameworks.
Ordinal Data vs. Nominal Data. Regarding ordinal data possessing inherent ordering characteristics ond (e.g., proprioceptive vectors), the event e, = (i, (,p:) defined as follows:
+1, if (or - (1)) Range(o:) > Co
p:
-, if (or 01- Range(o:) < -Co
(2)
0,
otherwise
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Seganenter
Obarvation
Event Predictur
Proticterr
Yi
y=
y,
2
h
F
h
F
h
F
Z0
Zo
A
Z
Z
A
1
Zz A
Eneader
Encuder
Eneader
0,
Figure 3. EAWM architecture that predicts the next observations and events. Given the length of the trajectory segment k, the sequence model outputs yc. which summarizes the embeddings Z, = ( 1 and A, (a..k..... a,-1]. The observation predictor predicts the next observation c, via the outputs of the sequence model y, and the outputs of the dynamics predictor 2. The representation model combines observation encodings with hidden states h, to obtain embeddings 2, The generic event segmentor identifies event boundaries, the starting and ending points of meaningful segments of the observation stream.
where (1) is the value of the i-th ordinal data at time and Range(o(t)) the range of ordinal data o()). From the above equations, event generation becomes straightforward once the threshold C, is specified (Hunt et al., 2025). For nominal data comprising mutually exclusive categories, we define an event et, change in the category of the corresponding observation. As listed in Table 12, the threshold for each modality is fixed across benchmarks.
3 METHODS
3.1 EAWM FRAMEWORK
Components Given trajectory segments of observations O, for_k+1,,... 0.], segments of actions A, Ar krt-k+,,-- ar). rewards rı, continuation flags of episodes c., and events ex, world models can be trained in self-supervised manner. The general formulation of each component of EAWM is outlined below:
Sequence Model:
h; Yr Fo A:-1)
Representation Model:
2. ~ @o (z) jo, h₂)
WM Dynamics Predictor:
2, pa lyr)
Reward Predictor:
i. Pe (Frlyee,
(3)
Continuation Predictor:
â, pe (é 2;,
Observation Predictor:
6, (0 po, 2x)
Event Predictor: Generic Event Segmentor:
é, pa (elerry, N141. 2,, 2:)
EA
ğ. go(0,, e.,
where h, is the hidden state, Yt the output of the sequence model, and the embeddings of observations. The tilde here indicates random variables sampled from their corresponding distribution. The WM part of Equation is general summary for modern world models, and almost all of them (Seo et al., 2023; Robine et al., 2023; Zhang et al., 2024b; Burchi Timofte, 2025; Hafner al., 2025) have included these five components shown above since they do exactly what needs to
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be done in MBRL. Notably, the EA part can be predicted with the output of the WM part. Hence, our avent-aware representation learning in Section 3.3 can be incorporated with these world models without theoretical difficulty. That is to say, our proposed framework is generally applicable, even to model-free RI.
Behavior Learning Our agent learns behaviors entirely from imagined trajectories generated by the world model, without requiring additional interactions with the external environment. If world models generated precise predictions of future observations and events in the environment, they would inherently learn robust and compact representations of history. We define the agent's state 8, as the integration of the sequence model outputs Y; with the observation embeddings Z,. This latent state serves as the input to standard reinforcement learning algorithms such as REINFORCE (Sutton et al., 1999), enabling policy optimization within the imagined environment. Importantly, the prediction of events or observations has no direct influence on policy training; that is to say. the computational overhead of the observation predictor and the event predictor can avoided during policy learning.
3.2 MULTI-MODAL EVENTS
To capture kinetic features explicitly, we introduce an automated process for event generation, thereby eliminating the need for manual laheling. As aforementioned, the notion of an event depends on the modality of the observations. For visual inputs, instead of using the primitive definition of events, we employ Adaptive Gaussian Mixture Models (AGMMs) (Zivkovic & Van Der Heijden, 2006) reduce false alarms from noise gradual changes in brightness. That is, we treat the event as statistically significant deviation from a learned multi-mode modal distribution of each pixel. The model maintains a mixture of K Gaussian components for each pixel:
Automated Event Generator
p(I((Is,1)))
wk.ti ( v:); Me,t, Ek,(,)
where Wh,r, 2 O are the mixture weights with Σk=1 Wh,t, = 1, Mkst, are the component means, and Ex.e,s are the covariance matrices. We compute the squared Mahalanobis distance DR.s between the next observation L1+1 yi) and the component means Mk,t, for each component k:
DR. = (L++1 (1,, ys) (a.).a) 2 1 W:) M.,t,i)
If Da. is larger than the threshold C7, we generate an event eiis 6f+1; = (Ii, Uc. t + 1, 1,ps) on that pixel and a new component will be generated; otherwise, L1+1 (I, y/s) matches a Gaussian component, say k, and the weight of component k will be increased. We also generate an event e when the matched component exhibits low weight in comparison to other components. To summarize, events generated at apatio-tempont locations where the model is either surprised (large DR.s) or uncertain (low weight), which aligns well with event-based systems (Muir & Sheik, 2025) that concentrate computation
informative changes rather than every frame and every pixel. As result, the event stream is sparse yet information-rich,, and less sensitive to irrelevant noise and slow illumination changes than the raw observations.
ord (i, I, L.):) As described in Section 2.2, for ordinal data such as joint angles and velocity, an event et, will be generated whenever the magnitude of change reaches the threshold C., For nominal data like tokenized inputs, an event e,, occurs whenever the type of information changes. This modular event-generation process enables our model flexibly accommodate diverse observation modalities. Full implementation details and configurations are provided in Appendix D.
nom
Notations For multi-modal observations o, (o(")) M M=1- where of (!) denotes the features of
the m-th modality and M represents the number of modalities, embeddings z, = {ze (m) can be
directly obtained from m-th modality of observations and yı, that is, 2, (m)
(m) qo(2)
(m)
(0 //!") .y).
Correspondingly, the distribution of the observations of the m-th modality is Po (ōrlye, {(m)) and
the distribution of the events of the m-th modality Pa (I7) (e(m)) (yt.Ne+1.2 2 (m) (m)
z
3.3 EVENT-AWARE REPRESENTATION LEARNING
Event Predictor To learn event-aware representations for world models, we design the event predictor to capture events in the observations. For each modality of events a(m)) design
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separate decoder network to estimate (f (e (e(.)))((y)))) (sg(y)))
Mr41,, (m) :4 (m)) E [0, 1]. the probability
of the class of every event éi, (m) To avoid spikes in the loss and encourage representations to be more
meaningful and predictable, the stop gradient operator sg(+) is used to prevent the gradients of targets from being backpropagated.
As aforementioned, we categorize the observations into three types, i.e., visual inputs D,, ordinal data Do. and nominal data P,. If the modality of observations falls into ordinal data, the cross-entropy loss is applied. If the modality of observations falls into nominal data like visual inputs, the focal loss (Lin et al., 2017) is applied:
(m)
(0)
Σ CrossEntropy(p; (m))
Focal(p) (m) m()))
PP
((m))
if m E Do if E U'
(4)
where N denotes the dimensionality (size) of the observation for the m-th modality. The total loss of event prediction is the weighted sum of the prediction loss of each modality:
M
(0)
=
g(m) cL (0),
(5)
m=
where the constant m!m) controls the scale of the event prediction loss of the m-th modality.
Generic Event Segmentor Excessive focus transient fluctuations can divert attention from critical events, just as humans struggle to read when there a sudden jolt (Katsuki Constantinidis, 2012). The start end time of meaningful slice of an observation stream, such as the jolt, is referred to as the event boundary (Zacks & Swallow, 2007). Human comprehension systems tend to form future prediction within event boundaries, whereas humans' ability of prediction and memory decreases at the event boundaries (Radvansky & Zacks, 2017). Motivated by this, we develop a generic event segmentor (GES) that automatically detects event boundaries. For each modality m, we notice that the number of events at (m) = >i= 1 (mm) occurs) can serve as an intuitive indicator of event boundaries. To keep the method computationally efficient, we do not introduce any additional
trainable parameters into the GES. Instead, we implement the GES as deterministic function of the events e (m).
go (m) 6(m)) o (m)) = g(a, (m)) (m))
(m)
(m) denotes the threshold of the percentage of events that determines whether the agent where Our
should attend events in the m-th modality. If g(a, (m)) ((())) O, we say that event boundary is detected. In this case, event prediction is devoid of meaning and should be suppressed. Therefore, Equation 4 with GES turns into:
(m)) (m) ((m))()). (o) Σ gim))(aa
a )
Furthermore, world models should prioritize dynamic events over static observations so as to improve accuracy on informative parts of the observations, rather than uniformly over all observations. However, when GES detects an event boundary where the priority of dynamic events is not suitable, the world model should reallocate attention from events to raw observations and focus on modeling raw observations. We implement this via an event-aware observation loss:
M mm
(o) = Co 6.)
Σ
wg(
(m)
[[((()) occurs) - 1] Co(or,s;
(m)
-1]
(8)
where E [0, is the event-aware weight to balance attention of the overall observations and the part of the observations where events take place. C' denotes the loss function of observations given by base world models by default if it exists. Otherwise, the mean square error of observations is calculated. Overall, the total loss of the EAWM
()) = (0 LEA (0)
(0m (,
where (!) denotes the loss functions of the base world model except the loss for observations.

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Published as a conference paper at ICLR 2026
Table 1: Game scores and human normalized aggregate metrics on the 26 games of the Atari 100K benchmark. We highlight the highest and the second highest scores among all baselines in bold and with underscores, respectively. The results on Atari 100k are based on the established re-implementation of the original version of DreamerV3 (Hafner et al., 2023) in PyTorch using the default hyperparameters. We follow the official implementation of Simulus (Cohen et al., 2025) and reproduce the results based on the suggested hyperparameters. All results of our experiments are reported over seeds.
Random
Harmony
EADream(Oann)
EASimulu:(Onn))
Alien
227.8
7127.7
607.2
744.1
1024.9
1179.3
776.4
691.8
740.0
Amidar
5.8
1719.5
95.3
225.8
130.8
166.3
144.2
101.7
148.2
Assault
222.4
742.0
1764.2
1526.
723.6
701.7
883.4
1528.
2112.7
2100
8503.3
1260.2
1096.9
1477.3
1590.3
Bank
524.9
Battle Zone
2360.
37187.5
11826
4702.0
11246.7
11563.3
13372.0
10142.3
14348.1
Boxing
0.1
12:1
97.5
86.9
$4.8
86.0
85.4
91.2
87.4
Reeakout
1.7
34.9
153.4
Chogper Command
709.7
3446.3
Cruzy Climber
10780.5
35829.4
76547.6
99167.8
81414.7
$4687.3
89038.6
77456.3
79274.4
Demon Attack
152.1
1971.0
5738.6
288.1
226.5
267.0
152.2
5045 9
$081.9
Freeway
0.0
29.6
32.3
33.3
9.5
31.3
32.4
Frosibite
274.1
1937.9
Gopher
5897.9
$300.
Hemo
1027.0
30826.4
6484.8
5621.8
46509
9863.3
88018
6626.9
7273.4
James Bond
29.0
302.8
191.2
427.4
331.8
327.8
337.2
677.3
612.4
Kangaroo
3035.0
167.6
S382.2
3851.7
5237.3
3875.6
7858.0
2569.6
8729.6
Kang Fu Master
22736.3
25172-2
18713.6
18917.1
22131.7
23434.6
20463.1
19456.2
Ms. Pacman
307
6951
962.5
1958.2
1813.3
2663.
1580.7
1331.9
1490.5
Pong
-20.7
14.6
18.0
20.4
17.1
20.0
20.1
19.1
20.0
69571.3
Qhert
13455.0
873.2
2234.0
3649.2
Road Runner
11.5
7845.0
14000.2
20673.2
14478.3
12478.3
12518.6
26325.0
20748.5
Seaquest
68.4
42064.7
1036.7
551.2
479.
S40.7
557.9
1602.7
140S
Down
533.4
11693.2
3757.6
3856.3
20183.2
10007.1
28408.2
3609.9
5742.4
#Supechuman(")
Meas ())
0.000
1.000
1.222
1,459
1.150
1,200
1,290
1,609
1818
Median(")
0.000
1.000
0.380
0.373
0.575
0.634
0.651
0.737
0,773
IQM())
0.000
1.000
0.673
0.641
0.521
(1.61
0.593
0.913
1.0014
Optimality Gap(L)
0.000
0.482
0.480
0.501
Table 2: Scores achieved across ten challenging tasks from Deep.Mind Control Suite with a budget of 500K interactions. We highlight the highest and the second highest scores among all baselines in bold and with underscores, respectively.
Task
CURL
DrQ-v2
TD-MPC2
DreamerV3
EADream(Ours)
Acrobot Swingup
5.1
128.4
241.3
210.0
452.1
Cartpole Swingup Sparse
236.2
706.9
790.0
792.9
825.8
Cheetah Run
474.3
691.0
537.3
728.7
874.3
Finger Turn Easy
338.0
448.4
820.8
787.7
916.6
Finger Turn Hard
215.6
220.0
865.6
810.8
921.5
Hopper Hop
152.5
189.9
267.6
369.6
311.5
Hopper Stand
786.8
893.0
790.3
900.6
926.2
Quadruped Run
141.5
407.0
283.1
352.3
648.7
Quadruped Walk
123.7
660.3
323.5
352.6
580.3
Walker Run
376.2
517.1
671.9
757.8
784.8
Mean(1)
285.0
486.2
559.2
606.3
723.8
Median(t)
225.9
482.8
604.6
743.3
805.3
4 EXPERIMENTS
We aim to evaluate the effectiveness and the generality our proposed framework in this section. Details for benchmarks and baselines are included in Section 4.1. To demonstrate the adaptability of our event-aware representation learning, we conduct experiments on two outstanding world models that were based quite different architectures. i.e., D3eamerV3 and Simulus, yielding EADream and EASimulus, respectively. A comprehensive evaluation of these world models is presented in Section 4.2. Ablation studies of the key elements proposed for EAWM are shown in Section 4.3.

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Published as a conference paper at ICLR 2026
14
27
65
RAChoan wil Foclen
34flwwn DMS
Deceakk
(a) No event predictor
(b) No GES
Figure 4. Ablation studies key components of EAWMs with 5 random seeds over 6 Atari games: Assault, Breakout, Gopher, Krull, and Ms Pacman, Up Down. The results show Simulus with the solid lines and EADream with the dashed lines.
4.1 BENCHMARKS AND BASELINES
Atari 100K Benehmark comprised of 26 different Atari video games (Bellemare et al., 2013) across a diverse range of genres. The benchmark challenges general algorithms to sample-efficient learning within 100K interactions with repeated actions, equivalent The scores for each task are computed by conducting 100 evaluation episodes at the end of training. Standard measurement for game is human-normalized score (HNS) (Mnih et al., 2015), calculated as HNS
hours of human gameplay.
agent random score
human score random scure"
Craftax is a recently released benchmark (Matthews al., 2024) that features an open- world environment like Minecraft. It challenges agents' capacity to conduct deep exploration, craft resources, and survive complex scenarios. Multimodal observations consist of an egocentric map and state information.
DeepMind Control Suite provides collection of classical continuous control tasks (Tunyasuvu- nakool et al., 2020) widely used in robotics and reinforcement learning research. In our experiments, we restrict algorithm inputs to high-dimensional image observations. Following established con- vention (Hansen et al., 2024), the number of environment steps is IM, which amounts to 500K interactions with repeated actions. We evaluate EADream on the hard task group introduced by Hubert et al. (2021), which are listed in Table 2.
DMC-GB2, as an extension of the DMControl Generalization Benchmark (Hansen Wang, 2021). consists of six continuous control tasks (Almuzairee al., 2024). Agents trained in fixed environment but evaluated across visually distinct test environments. We evaluate our EADream on the hard test set, including randomized colors and random substitutions of the original background for videos. Please refer to Appendix for visualizations of various test environments.
We choose competent baselines for the three benchmarks. On Atari 100K, besides DreamerV3 (Hafner et al., 2025) and Simulus (Cohen et al., 2025), world models include REM (Cohen et al., 2024), DIAMOND (Alonso al., 2024), and HarmonyDream (Ma et al., 2024). On Craftax 1M, we include the best model of TWM DreamerV3 and TD-MPC2 (Hansen et al., 2024), recent model-fres frree RL methods, CURL (Laskin et al., 2020) and DrQ-v2 ( (Yarats et al., 2022), are also included. On DMC-GB2, we include SVEA (Hansen et al., 202 1) and SADA (Almuzairee et al., 2024), specially designed algorithms on the benchmark that need pairs of original images and augmented images. For a fair comparison (Zhang et al., 2024b; Cohen et al., 2025), here we exclude lookahead search methods. Nevertheless, lookahead search techniques can be integrated with EAWMs the expense of computational burden.
(Dedieu et al., 2025). On DeepMind Control 500K, apart from
4.2 IMPLEMENTATION DETAILS AND RESULTS
EADream Our observation predictor 6 Pa (6 h 2.) is slightly different from the observation decoder 6, (ō [h, 2;) in DreamerV3 (Hafner et al., 2025). EADream predicts future observa-
tions directly from the prior states z;, and thus we name this dynamic model RSSM-OP, To achieve
(m) compututational efficiency, implement simple form of GES: g(a 0
T(a
(m) (m)
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Please refer Appendix A for more details. As Table 2 displays. EADream reaches a mean score of 723.8 and a median score of 805.3, setting new soate-of-th--ane results among all RL methods on these challenging tasks from DeepMind Control Suite. On DMC-GB2, EADream outperforms DreamerV3 by a large margin, highlighting its robustness to visual distractors and generalization across visual variations. Beyond our expectations, E.DDream even outperforms SADA, an algorithm specifically designed for the benchmark. Unlike SADA, EAWM does not rely on paired original and augmented images, demonstrating that strong generalization can be achieved without additional supervision.
EASimulus Integrating Simulus into EAWM is straightforward, requiring only the integration of the event predictor and GES. Specifically, the output of the GES increases as events become sparser, allow-
ing them to be better highlighted: g( (m) (m) Athr
I(at (m) (m)) < //rrsin
clip
(m) Eao
where O is a coefficient to smooth the curve of oz (m) and stablize the training process. Other details are provided in Appendix B. Remarkably, EASimulus obtains a mean numan-normalized score of 1.818, setting new record and reaching a superhuman IQM score for the first time among MBRL methods. addition, EAWM provides boost the performance of the satte-of-the-eee method Simulus and reaches score of 7.23% in Craftax 1M, indicating that multi-modal event awareness facilitates policy learning.
4.3 ABLATION STUDIES
We discuss the effectiveness of key components of EAWM. We randomly select 6 games from Atari, as illustrated in Figure 4, and 4 tasks from DeepMind Control Suite, as shown in Figure 5.
No Event Predictor In this case, the GES in- fluences the loss function for observation pre- diction in Equation 8. Figure 4a shows the decrease in mean HNS scores of both world models by about 0.4 without the event predictor. We observe that representation learning from event prediction plays an essential role in envi- ronments where events provide sufficient infor- mation for reward prediction, such as Breakout and Krull. Furthermore, these results show how kinetic features reshape and accelerate policy learning.
Figure 5. Ablation studies on key components of EAWM on DeepMind Control Suite.
No GES The GES stabilizes the training pro-
cess of world models and provides robust event-aware representations for policy learning. As shown in Figure 4b, the median HNS scores grow slowly and display large fluctuations the absence of GES. This stability is particularly critical in continuous control robotics tasks, where recovering from a single erroneous action often requires executing a long sequence of corrective steps.
Without Observation Prediction We notice a performance drop if EAWM is configured make observation reconstruction. Specifically, the mean score of four tasks declines from 737.2 to 519.5, as shown by the orange line in Figure In addition, we conduct an experiment on DreamerV3 with RSSM-OP in Table 13, demonstrating that the performance improvement of EAWM mainly stems from the joint modeling of observations and events, not from RSSM-OP alone. These results imply that event prediction and observation prediction are tightly coupled in event-aware representation learning.
5 RELATED WORK
Model-based Reinforcement Learning Sample efficiency has become increasingly important in reinforcement learning, especially for real-world applications (Sutton, 1991; Hafner, 2022). The first world model (Ha & Schmidhuber, 2018) showed that combining latent dynamics with generative observation model enables agents to plan through imagination, sparking broad interest in MBRL (Mo- erland et al., 2023). Dreamer, a notable series of methods (Hafner et al., 2019a; 2020; 2025), which is based on the recurrent state-space model, constructed dynamics models in latent space. Descendants

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of RSSM (Gumbsch et al., 2023; Sun et al., 2024), were proposed to learn hierarchical and robust latent representations. Encouraged by the huge success of Transformer architecture (Vaswani, 2017), several works (Chen et al., 2022; Micheli et al., 2023; Sco et al., 2023; Robine et al., 2023; Zhang et al,, 2024h; Burchi & Timofte, 2025) attempted to use a transformer-haa hased world model to learn the dynamics in environments. In parallel, TD-MPC2 (Hansen et al., 2024) predicts future states rather than observations, achieving strong performance in state space but struggling when applied directly to image-based environments. Following REM Cohen al. (2024), Simulus Cohen al. (2025) surpasses previous methods on Atari 100K and Craftax 1M benchmarks. DyMoDreamer (Zhang al., 2025) is the most closely related work to our approach, which enhances DreamerV3 (Hafner et al., 2025) by encoding differential observations obtained via an inter-frame differencing mask, demonstrating the effectiveness of dynamic features. Instead of treating inter-frame differences as inputs, we train EAWM to predict both future observations and future events. Furthermore, our method is totally distinct from the above methods - EAWM is not a standalone architecture, but general framework that can endow different world models with general understanding of more abstract events.
Representation Learning for Reinforcement Learning Representation learning is key sample- efficient deep RL (Nikishin et al., 2022; Wang et al., 2024a), yet non-stationary targets and boot- strapping destabilize training (Voelcker et al., 2024). Early methods such UNREAL (Jaderberg et al., 2017) introduced specialized auxiliary tasks, while contrastive methods (Laskin et al., 2020; Stooke et al., 2021; You et al., 2022; Eysenbach et al., 2022) and image augmentations (Yarats et al.. 2021; 2022) improved consistency and robustness. Recent work (Wu et al., 2023; Kim et al., 2024) also explores pretraining models with abundant videos for downstream visual control tasks. With increasing empirical (Ni et al., 2024; Fang & Stachenfeld, 2024; Kwon et al., 2025) and theoreti- cal (Uehara al., 2022; Liu et al., 2023) evidence proving the benefit of using predictive auxiliary tasks, nexl-observation prediction has become common choice for modern MBRL. However, these methods often struggle to extract the task-relevant structure in high-dimensional inputs (Zhang et al., 2024a). Alternatives like depth (Poudel et al., 2024) motion prediction help, but they remain limited to static visual inputs and require additional labels. In contrast, our work introduces event prediction as label-free auxiliary task, enabling agents capture task-relevant dynamics and structure without reliance on manual annotations.
6 CONCLUSION
In this paper, we have introduced EAWM, general framework that enables world models to capture key events from the environments. EAWMs master tasks across different domains for control, be it discrete or continuous. Specifically, EAWM achieves a 13%, 10%. 19%, and 45% performance boost on Atari 100K, Craftax IM, DeepMind Control 500K, and DMC-GB2 500K, respectively. Moreover, EAWMs have established new state-of- oftte-of-thh-arr art results across these benchmarks. Overall, EAWM paves the way for building RL agents that react to events rather than static images.
We identify three limitations of our work for future research. First, as this is the initial attempt to integrate event segmentation into world models for policy learning, our implementation of GES is deliberately simple for efficiency. Prior approaches to event boundary detection, such as using changes in reconstruction error of video snippets (Wang et al., 2024b), are more specialized but contain substantial redundancy (Zheng et al., 2024). Future work may consider modeling the function of GES directly with neural networks to achieve both efficiency and expressiveness. Second, although EADream and EASimulus are trained with fixed hyperparameters across domains, developing unified model capable of solving multiple tasks by effectively sharing common knowledge is still challenging (Sridhar et al., 2025). Finally, another promising direction is combine EAWM with large pretrained vision-language models, which could enhance generalization and enable more flexible grounding across modalities (Contributors, 2025; Chen al., 2025; Yang et al., 2025).
ACKNOWLEDGMENT
This work was partially supported by the National Natural Science Foundation of China (No. 62293545, 62425117, 62301 299, and 62506205), the China Postdoctoral Science Foundation (No. 2025T1 184466), the Postdoctoral Fellowship Program of CPSF (No. GZB20250393).
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REPRODUCIBILITY STATEMENT
We have made every effort to ensure the reproducibility of our work. We include theoretical proofs of our EADream in Appendix A.2 and A.3. The architectures of EADream and EASimulus are fully specified in Appendix A.4 and B. with hyperparameters provided in Appendix A.5 and B.3. Pseudocode for the training protocol of EAWM is included in Appendix C, and the configurations of multi-modal event prediction are Appendix D. Extended results, including Atari 1M and all tasks from DeemMind Control Suite, are provided in Appendix and H. More experimental settings, including baselines and computational resources, are described in Section and M. For transparency, training curves are shown in the Appendix K and L. In addition, results and visualizations are included in the supplementary materials. We will release our code and checkpoints to facilitate replication of experiments.
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APPENDICES
A EADream
18
EASimulus
23
Algorithm
25
D Multimodal Events
25
Baselines
25
Hyperparameter analysis
27
G Atari 1M
28
H Extended results on DeepMind Control
29
I DMC-GB2
30
J Additional Results on Atari 100K
32
K Atari 100K Curves
33
DeepMind Control 500K Curves
35
M Computational Resources
35
Visualizations
35
Use Large Language Models
43
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A EADREAM
A.1 FRAMEWORK
Components With the length of trajectory segments set to I, segments of observations O, and actions A, in Section 3.1 degenerate into og and a,, respectively. DreamerV3 Hafner et al. (2025) uses the hidden state h, as the output yt of the sequence model in Equation equation 3. That is to say, y: hy Consequently, the formulation of each component turns into:
Sequence Model:
h Fo Zz-1, ax-1)
Encoder Network:
~ (Ze or. h.)
Dynamies Predictor:
2, po (2, (h,)
DreamerV3
Reward Predictor:
ft ~ pe (fel Fee Zt)
(10)
Ending Predictor:
ê ~ po (&el by, Z,)
Observation Predictor:
6 pa (ô, h 2x)
Event Predictor:
é; pe (ê, (e, 2e, 2u)
EA
Generic Event Segmentor:
8. go go oc, ex)
The first five components above are the same as that of DreamerV3 Hafner et al. (2025). Notice that our observation predictor 6, po h 2x) is different from the observation decoder of DreamerV3 (Hafner et al., 2025) denoted by ô ~ po(ó,/h, 2.). EADream predicts the next observations 6, without access them, while DreamerV3 makes reconstruction of the observations from z. Due to the subtle change in the formulation, it necessary to reinterpret the related models and the training objectives proposed by DreamerV3, as we do in Appendix A.2 and Appendix A.3.
Loss Functions For DreamerV3, we implement a simple form of GES:
g(a, (m)
(m)
= I(a
((a
(m)
< a
(m)).
(11)
From Equation equation 8, the event-aware prediction loss for observations
(0) E, + wl(aa (TT))
(()) 1) O Ex:
(12)
where E; = (Et, Nt and E,, the loss function of or, and ôg,s. If every observation changes or the GES is disabled, then observation prediction loss (0) is equivalent to the original loss for observations in DreamerV3.
A.2 REINTERPRETAATION OF THE REPRESENTATION MODEL AND DYNAMICS MODEL
Since the recurrent state space model with observation prediction can be seen as a Markov process as shown in Figure 6, the encoder can be formulated as - 11la-aaa-p-- -1: 01.7,, Og)
I2=1 ((se, act) - q(s: h,), where h, h( a;-1) and terministic functions. Therefore, the distribution of s, can be obtained we know the distribution of the stochastic state 2,. We parameterize the distribution of z, via representation model go or h₂). where h, = h(s at-1) can be implemented as recurrent neural network. Similarly, we can obtain p(s//S(-1, 1)) from p(zz\S a plz h which necessitates the dynamics model Po(2, h.). Furthermore, we have (g(s or, h, [p(ss h,)) = (Kl(q(z) or h,) [p(z, h2)) due to the implementation of s; in DreamerV3, which is the concatenation of h, and zt-
s(h,, 2.) are de-
A.3 PROOF OF TRAINING OBJECTIVES
Though (Hafner et al., 2019b) has proved an objective for world model training in Dreamer algorithms, it is unclear what the training objective is to learn world model utilizing RSSM for observation prediction (RSSM-OP). direct objective to maximize the log-likelihood of video data. Therefore, we derive the Evidence Lower BOund (ELBO) of the log-likelihood conditioned on the action sequence and the first frame.
Since want to predict the next frame conditioned on the current state and action, the latent dy- namics model is p(O1.T, Sp:T-1 |a op)
-IT² p(or a On) p():((s-1)) 8 a 0n)
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a
ho
So
h
h
Z
2
0
0
Q
Figure 6. RSSM with observation prediction.
Ih=
222)), where a.) is defined as p(soloo). Accord-
ingly, the variational posterior is ((SOTT |.0 01.7; 00) = I1: ((e) acr)), where define ( acv) as {(Su) Using importance weighting and Jensen's inequality, the ELBO of the likelihood of the image conditioned on the first frame and history of actions is:
In aa o0) e In plss m (..0)))
Il
a
In 1oBaaT-l/ooT--10 ( )p 1 a
(Soor 10:0--1000:-000
= In (alearrrlaaar-eee (00rs 1, http://///.........///./o....///
- (10T-1000)
In Han -aai--ngsss----ee -n
Σ Eylss (DL (q(s alt-) a(-))
For T xc. always minimize the KL divergence of the latent dynamics models p(5(-1/S(-2, 2(22 and the variational posterior substitute for t. The second term will be
Set t' = and then
Σ Ey(se alggeng 1) [DL (g(s)logn, a a-1)))
We sample batch from episodes, and minimizing the KL divergence helps improve the prediction of future frames across different batches. Therefore, the modified objective is to maximize
T
Σ (E (B [Inp(o:/s-11, DKL (g(s:)ost, acx a.-1)))
DKa. ((a.(y(ss/om]]|((ssooo)))
In fact, we can (So oo) p(so on) save the hassle of dealing with inputs with different dimensions. Therefore, our training objective for observation prediction is:
(Ey(n (loceaa) [Inp(or|st-1) a)-1) DRL (g(s) Ogg, a [p 1, a 1))) (13)
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which takes the same form the training objective for observation reconstruction in Dream- erV3 (Hafner et al., 2025), which is listed below for comparison:
st-1, (De [Inp(or/s)) DKL (g(5: a.
(14)
A.4 ARCHITECTURE
The representation model DreamerV3 (Hafner et al., 2025) consists of the observation encoder and the representation predictor. Table shows the process of the image encoder to obtain embeddings of an image, since we focus on dealing with high-dimensional visual inputs for experiments on EADream. Here, is the reduction ratio of the ChannelAttention submodule and k: is the kernel size of the Spatial submodule. The representation predictor takes as input the embeddings and the deterministic hidden states h, obtain the posterior stochastic hidden states Zr, described in Table 4. LayerNorm denotes layer normalization (Ba, 2016), and SiLU is short for sigmoid-wiighter linear units, an activation function which is formulated as SiLU(x) Ite
Table 3:
Structure of the observation encoder
Stage name
Output size
Submodule
ChannelAttention, SpatialAttention,
CBAM
64 x 64
4 x 4, 32, stride 2 LayerNorm + SiLU
Convl
32 x 32
x 4, 64, stride 2 LayerNorm + SiLU
Conv2
16 16
4 128, stride LayerNorm + SiLU
Conv3
8 8
4 x 4, 256, stride 2 LayerNorm + SiL.U
Conv4
4 x
ChannelAttention, = SpatialAtteention,
CBAM
Flatten
4096
the embeddings of image
Table 4: Structure of the representation predictor
Stage name
Output size
Submodule
Inputs
4096 Nattr
Concatenate f and the embeddings of image
Linear
FC1
Naid
LayenNorm SiLU
Linear
FC2
Zuum
Zetass
LayerNorn + SiLU
Reshape
Both the observation predictor and the event predictor are expected to output tensors of the same size. To that end, implement similar networks for observation and event prediction, depicted in Table 5 and Table 6. Table 7 displays ML. structures of the reward predictor and continuation predictor.
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Table 5: Structure of the observation predictor
Stage name
Output size
Submodule
Inputs
Nstoc + Ndster
Concatenate h4 and 3
Linear Reshape into tensors of 256 channels
FCI
4 x 4
4 x 4. 128, stride 2 LayerNorm + SiLU
Deconv
8 x 8
x 4, 64, stride 2 LayerNorm + SiLU
Deconv2
x
4 x 4, 32, stride 2
Deconv3
32 x 32
LayerNorm + SiLU
Deconv4
64 x 64
4 x 4, 3, stride 2
Table 6: Structure of the event predictor
Stage name
Output size
Submodule
Inputs
2Notch + Naater
Concatenate h2 and &
Linear
FC1
4 x 4
Reshape into tensors of 256 channels
4 x 4, 128, stride LayerNorm + SiLU
Deconv
8 x
4 x 4. 64, stride LayerNorm + SiLU
Deconv2
16 x 16
4 4, 32, stride LayerNorm + SiLU
Deconv3
32 x 32
4 x 4, 1, stride 2 Sigmoid
Deconv4
64 x 64
Table 7: Reward predictor and continuation predictor
Details of ML.P
Reward predictor
continuation predictor
Inputs
Nu + Naeter
Nattch + Nalterr
Hidden units
N
Nu
Outputs units
255
1
Activation function
SiLU
SiLU LayerNorm 2
Normalization
LayerNorm
Layers
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A.5 HYPERPARAMETERS
Table shows the hyperparameters of EADream. These hyperparameters are fixed on both Atari 100K and DeepMind Control benchmarks.
Table Hyperparameters in our world model, EADream. Nawch de facto denotes the dimension of the flattened version of z₁ That is to say, stcohh mum Zelass for discrete representations, which is our choice. Nawch
Zum when continuous representations are applied.
Type
Hyperparameter
Value
Image size
64
64 3
Batch size
16
Batch length
64
General
Gradient Clipping
1000
Discount factor 7
0.997
Lambda
0.95
Number of stochastic variables Znum
64
Classes per stochastic variable Zelass
32
Number of deterministic units Ndeter
512
Number of stochastic units Nstoch
2048
Number of MLP units Nuni
512
Number of RSSM units Nui Imagination horizon First frame prediction
512
15
False
World model
Event prediction
True
Updatation per interaction
1 True AdamW
Harmonizers
Optimizer
AdamW episilon
1 x 10-8
AdamW betas (B1, (2)
(0.9, 0.999)
Learning rate
x 10
Gradient clipping
1000
Event prediction weight &
0.5
Observation prediction weight &.
1.0
Event-Aware Mechanism
Focal loss alpha
0.15
Focal loss gamma y
4
Event-aware weight
0.5
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Simulus (Cohen et al., 2025) is a boken-based world model that maps observations o, and actions ar into tokens. We denote the number of tokens of observations as Kn, the number of tokens of observation as K,, and the embedding dimension as D, where K, E k=!m) and k=!m)) denotes the number of tokens of observations that are in the m-th modality. Then the number of tokens in Published as a conference paper at ICLR 2026
B EASIMULUS
B.1 FRAMEWORK
Components Simulus (Cohen et al., 2025) is a modular world model that separates optimizations of the encoder-decoder network and the other parts the world model. The sequence model of Simulus is a retentive network (Sun al., 2023) augmented with parallel observation prediction (Cohen et al., 2024). We can also derive EASimulus from Equation equation by omitting yr in the representation model and making predictions depending only the output of the sequence model yı:
Sequence Model:
h Yr Fo Z, At-1)
Representation Model:
2, (0 o.))
Dynamics Predictor:
2. ~ p0 yz)
Simulus
Reward Predictor:
Ft (fil))y))
(15)
Continuation Predictor:
â (e
Observation Predictor:
6, (0,
Event Predictor:
é,
EA
Generic Event Segmentor:
&. (o,,,,,,,
In Simulus, the representation model and the observation predictor form a pair of an encoder-decoder network. We follow their training protocol and jointly train the other parts of the world model besides the event predictor.
Loss Functions Intuitively, if events sparsely distributed, agents should pay more attention to these events. In EASimulus, we implement GES as below:
g(a,
(m)) Ou
(me)
T(a
(m))
<
thr
(m))
(16)
arsinh
clip
Ees
where clip(ær, Tmin: Tmux) is function that limits within the range [Mimin; Xmmx), and arsinh(x:) = In( + vI + x²) is the inverse hyperbolic sine function. >> is coefficient smooth the curve of (m) and stablize the training process. Since the embeddings of current observations are learned independently in Simulus, we keep the same prediction loss of observations in EASimulus.
B.2 ARCHITECTURE
one environment step is K = K, + K.. To our surprise, the simple structure in Table is qualified to make accurate prediction of events. In addition, Table 10 shows the structure of the reward predictor and the continuation predictor.
Table 9: Event segmentor in EASimulus for the m-th modality.
Type of Data
Ordinal Data
Nominal Data
Inputs Hidden units Outputs units Activation function
2 (m)
D
2 x K(()) D
4D 3 SiLU LayerNorm 2
4D
SiLU LayerNorm 2
Normalization
Layers
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Table 10: Reward predictor and continuation predictor in EASimulus.
Details of MI.P
Reward Predictor
Continuation Predictor
Inputs
K D
K x
Hidden units
2D
2D
Outputs units
129
1
Activation function
SiLU Symlog
SiLU
Normalization
LayerNorm
Layers
2
2
B.3 HYPERPARAMETERS
For EASimulus, we only tune hyperparameters that our methods introduce, listed in Table 11:
Table 11: Hyperparameters in EASimulus.
Type
Hyperparameter
Value
First Observation prediction
False
World model
Event prediction
True
Event prediction weight B Observation prediction weight &
1.0
1.0
Event-Aware Mechanism
Focal loss alpha a
0.15
Focal loss gamma ?
4
Smoothing coefficient Ca.
0.0005
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C ALGORITHM
The training process of EAWM is sketched out in Algorithm 1.
Algorithm EAWM Training
An initialized replay buffer repeat
Input:
0o: To: cD < env. reset ()
Initialize parameters Aut outteed using On (Section 3.3)
for = 0 to MAX do
a, ~ (a.)ogrr act)
0.41, M++., C++1 + env.
e - Aut onntommteePPocess generateeeeee (0.+1)
if C++1 = 0 then
fm =++1
break
end if
Sample B data of length T from D
Predict {ōr, myg 2,, 2e, fr. & Jt=k k++-1 # Equation equation 3
Compute loss from the base world model (@)
Compute event prediction loss with GES (.) Equation equation 7
Compute event-aware prediction loss for observations (.) Equation equation 8
Compute total loss (()) # Equation equation
Update parameters 0 Actor-critic learning in imagined trajectories end for D. add {o,, ar, et, rt, Ce ) until Training is stopped
D MULTIMODAL EVENTS
Table 12: Settings of related multimodal events
Modality
Type
Symbol
(m) Threshold O
Weight Coefficient g(m)
Image
Visual Inputs
0.5
1.0
Continuous Variables
Ordinal Data
Et
1.0
0.1
Categorical Variables
Nominal Data
0.5
0.1
7,
2D Categorical Variables
Nominal Data
nom
0.5
0.1
E BASELINES
E. DREAMERV3
We used the default parameters and reproduced the results based on the implementation of DreamerV3 in Py Torch, which performs slightly better than the original official implementation of DreamerV3. In the original version of the paper (Hafner et al., 2023), DreamerV3 reported HNS mean score of 112%.
E.2 HARMONYDREAM
Since no hyperparameter introduced HarmonyDream (Ma et al., 2024), we implemented harmonizers following recommendations from the authors. We reproduced the results based on the
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aforementioned implementation of DreamerV3 with harmonious loss, suggested by the authors in their articles.
E.3 TD-MPC2
Results for eight out of tasks from DeepMind Control Suite be found the official repository on GitHub, except Hopper Hop and Hopper Stand. We follow the official implementation of TD- MPC2 (Hansen al., 2024), use the default hyperparameters, and select the default 5M parameters for the single task.
E.4 SIMULUS
We follow the official implementation of Simulus (Cohen et al., 2025) and reproduce the results based the default hyperparameters. We reproduce experiments Atari 100K.
E.5
DREAMERV3-RSSM-OP
Table 13:
Game scores and human normalized mean score for DreamerV3+RSSM-OP on the 6 games from the Atari 100K.
Game
EADream
DreamerV++RSSM--P
DreamerV3
Assault
883.4
792.5
723.6
Breakout
71.8
20.8
26.9
Gopher
4415.8
5287.3
4074.9
Krull
8729.6
7612.1
7796.6
Ms Pacman
1580.7
1429.3
1813.3
Up N Down
28408.2
20696.9
20183.2
Mean (t)
2.501
1.951
1.901
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F HYPERPARAMETER ANALYSIS
In terms of practical effort, tuning the additional hyperparameters required only a small number of validation runs 6 Atari games that we used in our ablation studies: Assault, Breakout, Gopher, Krull, Ms Pacman, and Up N Down. Compared to previous work, such as DreamerV3 (Hafner et al., 2025), which tunes the base world model across many hyperparameter settings, this cost is relatively minor. In our experiment, once reasonable default for the hyperparameters is chosen on these Atari games, the values of the hyperparameters transfer well tasks across DMC, Craftax, and DMC-GB2.
We posit that a universally applicable configuration exists for diverse scenarios. We use the default values of C 16 and w = 0.5 for all experiments across benchmarks. As shown in Tables 14 and 15, our EADream consistently outperforms DreamerV3 even if the hyperparameters introduced by our method change significantly.
Table 14: Game scores and human normalized mean score for different values of C₁ the games from the Atari 100K.
Game
EADream (C₁ = 8)
EADream (C₃ 16)
EADream (C₁ 32)
DreamerV3
Assault
781.5
883.4
821.1
723.6
Breakout
43.2
71.8
23.1
26.9
Gopher
2997.1
4415.8
3316.5
4074.9
Krull
7043.0
8729.6
7575,8
7796.6
Ms Pacman
1568.6
1580.7
1566.5
1813.3
Up N Down
38616.1
28408.2
42507.2
20183.2
Mean (1)
2.082
2.501
2.144
1.901
Table 15: Game scores and human normalized mean score for different values of w the games from the Atari 100K.
Game
EADream 0)
EADream (w 0.5)
EADream (w 1)
DreamerV3
Assault
942.8
883.4
639.1
723.6
Breakout
49.2
71.8
16.3
26.9
Gopher
3741.1
4415.8
2118.7
4074.9
Krull
8244.2
8729.6
8759.5
7796.6
Ms Pacman
1751.3
1580.7
2027.8
1813.3
Up Down
19499.0
28408.2
37913.0
20183.2
Mean (t)
2.132
2.501
2.081
1.901
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G ATARI 1M
In this section, we investigate data scaling properties of the EAWM by increasing the number of interactions from 100K to IM. To our surprise, EADream performs much better than DreamerV3, as shown in Table 16. This result demonstrates that EAWM could be beyond the sample-efficient regime.
Table 16: Game scores and human normalized aggregate metries on the Atari 1M. We highlight the highest and the second highest scores among all baselines in bold and with underscores, respectively. The results of DreamerV3(1M) are obtained from the official data of DreamerV3 (Hafner et al., 2025), where the agent was trained with budget of 200M environment steps.
Game
Random
Human
Ours(100K)
DreamerV3(IM)
Ours(1M)
Alien
227.8
7127.7
776.4
1978.2
1550.2
Amidar
5.8
1719.5
144.2
265.5
672.8
Assault
222.4
742.0
883.4
1949.1
3369.2
Asterix
210.0
8503.3
1096.9
2917.3
6605.5
BankHeist
14.2
753.1
742.6
569.5
1272.5
BattleZone
2360.0
37187.5
13372.0
5261.8
39070.0
Boxing
0.1
12.1
85.4
96.6
93.6
Breakout
1.7
30.5
71.8
25.1
416.8
ChopperCommand
811.0
7387.8
904.0
4442.8
647.0
CrazyClimber
10780.5
35829.4
89038.6
108468.5
112524.0
DemonAttack
152.1
1971.0
152.2
644.4
2323.8
Freeway
0.0
29.6
0.0
7.9
34.0
Frostbite
65.2
4334.7
692.6
3741.9
3989.1
Gopher
257.6
2412.5
4415.8
14935.7
83756.6
Hero
1027.0
30826.4
8801.8
13600.5
20564.1
Jamesbond
29.0
302.8
337.2
465.6
688.5
Kangaroo
52.0
3035.0
3875.6
5432.7
5079.0
Krull
1598.0
2665.5
8729.6
13389.3
11534.0
KungFuMaster
258.5
22736.3
23434.6
35395.8
46526.0
MsPacman
307.3
6951.6
1580.7
3165.6
3449.9
Pong
-20.7
14.6
20.1
9.4
21.0
PrivateFye
24.9
69571.3
-47..5
882.4
5595.0
Qbert
163.9
13455.0
1664.4
5006.7
14411.0
RoadRunner
11.5
7845.0
12518.6
21404.0
19359.0
Seaquest
68.4
42054.7
557.9
1580.6
2387.8
UpNDown
533.4
11693.2
28408.2
115045.3
416219.0
#Superhuman(t)
N/A
12
10
17
Mean(t)
0.0
1.000
1.290
2.213
5,273
Median(t)
0.0
1.000
0.651
0.782
1.188
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H EXTENDED RESULTS ON DEEPMIND CONTROL
For more comprehensive evaluation of EAWM, we conducted an extensive experiment on all 20 tasks from the DeepMind Control Suite. As demonstrated in Table 17, EAWM has state-of-the- art result on the DeepMind Control Suite. Moreover, EAWM achieves the highest scores on half of the tasks among the baselines and performs consistently well.
Table 17: Scores achieved for all 20 tasks from DeepMind Control Suite with a budget of 500K interactions. We highlight the highest and the second highest scores among all baselines in bold and with underscores, respectively.
Task
CURL
DrQ-v2
DreamerV3
TD-MPC2
EADream (Ours)
Acrobot Swingup
5.1
128.4
210.0
241.3
452.1
Cartpole Balance
979.0
991.5
996.4
993.0
999.4
Cartpole Balance Sparse
981.0
996.2
1000.0
1000.0
1000.0
Cartpole Swingup
762.7
858.9
819.1
831.0
871.4
Cartpole Swingup Sparse
236.2
706.9
792.9
790.0
825.8
Cheetah Run
474.3
691.0
728.7
537.3
874.3
Cup Catch
965.5
931.8
957.1
917.5
966.9
Finger Spin
877.1
846.7
818.5
984.9
596.7
Finger Turn Easy
338.0
448.4
787.7
820.8
916.6
Finger Turn Hard
215.6
220.0
810.8
865.6
921.5
Hopper Hop
152.5
189.9
369.6
267.6
311.5
Hopper Stand
786.8
893.0
900.6
790.3
926.2
Pendulum Swingup
376.4
839.7
806.3
832.6
835.0
Quadruped Run
141.5
407.0
352.3
283.1
648.7
Quadruped Walk
123.7
660.3
352.6
323.5
580.3
Reacher Easy
609.3
910.2
898.9
982.2
937.7
Reacher Hard
400.2
572.9
499.2
909.6
654.9
Walker Run
376.2
517.1
757.8
671.9
784.8
Walker Stand
463.5
974.1
976.7
878.1
966.6
Walker Walk
828.8
762.9
955.8
939.6
942.6
Mean(t)
504.7
677.4
739.6
743.0
800.5
Median(t)
431.8
734.9
808.5
831.8
872.8
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I DMC-GB2
DMC-GB2 (Almuzairee et al., 2024) provides various test environments that are visually distinct from the training environment, shown in Figure 7, and challenges RL agents to the ability of visual generalization. Algorithms such as SVEA (Hansen et al., 2021) and SADA (Almuzairee et al., 2024) are specifically designed for this setting and require paired -aggeente images. In contrast, EADream is applied to DMC-GB2 directly, without any modifications during migration from DeepMind Control, and trained under fixed hyperparameters across five random seeds. Besides DreamerV3 (Hafner et al., 2025), we compare against Dy MyMormeeer (Zhang et al., 2025), world model architecture that exploits temporal information via frame differences. As shown in Tables 18 to 20, EADream even slightly outperforms SADA, the state-of-the-art method tailored for this benchmark- an outcome that exceeds our expectations. We attribute this generalization ability to our Generie Event Segmentor (GES), which capture event structures and filter out irrelevant events. Unlike purely reconstruction-die drriven objectives, which tend to overfit to pixel-level variations, GES focuses on changes that correspond to meaningful dynamics in the environment. This enables EAWM to disregard spurious fluctuations in the background-such as texture shifts, lighting changes, or irrelevant object motions- that do not alter the underlying task. By emphasizing task-relevant kinetic features, the model learns to prioritize interactions that matter for control, such as the movement of the agent's body or objects critical task success. As result, EAWM demonstrates robust visual generalization on DMC-GBI suggesting strong potential for transfer to broader environments and real-world applications where background distractors are pervasive.
Figure 7. Snapshots of the Cheetah Run task in DMC-GB2 (Almuzairee et al., 2024). The test environments consist of Color Easy, Color Hard, Video Easy, Video Hard, Color Video Easy, and Color Video Hard (from left to right). Color refers to environments with randomized colors, while Video refers to the substitution of the original background for video from natural environments.
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Published as a conference paper at ICLR 2026
Table 18: Scores achieved in Color Hard environments.
Task
DrQ
SVEA
SADA
DreamerV3
DyMoDremmer
EADream (Ours)
Cartpole Swingup
441
478
716
432
495
774
Cheetah Run
178
133
239
417
593
567
Cup Catch
520
779
961
93
175
914
Finger Spin
466
802
868
218
681
576
Walker Stand
527
861
963
957
969
964
Walker Walk
265
667
825
617
766
705
Mean(t)
400
620
762
456
613
750
Table 19: Scores achieved in Video Hard test environments.
Task
DrQ
SVEA
SADA
DreamerV3
DyMoDreamer
EADream (Ours)
Cartpole Swingup
98
259
363
296
138
449
Cheetah Run
25
28
82
228
127
240
Cup Catch
111
416
662
53
92
288
Finger Spin
263
566
205
400
Walker Stand
154
429
702
816
809
872
Walker Walk
36
264
270
569
664
613
Mean(1)
72
277
441
343
339
477
Table 20: Scores achieved in Color Video Hard environments.
Task
DrQ
SVEA
SADA
DreamerV3
DyMoDreamer
EADream (Ours)
Cartpole Swingup
94
294
426
338
137
437
Cheetah Run
26
44
99
435
356
531
Cup Catch
122
484
697
16
87
573
Finger Spin
307
633
63
149
398
Walker Stand
170
659
906
946
837
952
Walker Walk
42
421
686
614
778
648
Mean(t)
368
575
402
391
590
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Published as a conference paper at ICLR 2026
J
ADDITIONAL RESULTS ON ATARI 100K
Mean (1)
Median (f)
Interquartile Mean (1)
Optimality Gap (1)
EASimulus(ours)
Simulus
DIAMOND
EADream(ours)
HarmonyDream
DreamerV3
1.25 1.50
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Figure 8. Mean, median, and inter- mean (IQM) human normalized scores and the optimality gap (Agarwal et al., 2021) with 95% stratified bootstrap confidence intervals on the Atari 100K benchmark.
1.00
DreamerV3
EXDrezmiours)
Simulus
[armmonyDremm Dream
DIAMOND
EA (imulus(ours)
Fraction of runs with scare
0.75
0.50
0.25
2.60
Human Normalized Score (T)
Figure Performance profiles (Agarwal et al., 2021). The curve of each algorithm shows the proportion of runs in which human- numan-normalized scores greater than the given score threshold.
P(EASimulus Y
Simulus
DIAMOND
Algorithm
EADreammours)
HarmonyDream
Dreamer V3
0.00
0.25
0.50
0.75
1.00
Figure 10. Each row represents the probability of improvement (Agarwal et al., 2021) that our algorithm outperforms the corresponding baseline in randomly selected task from all tasks with 95% stratified bootstrap confidence intervals.
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Published as a conference paper at ICLR 2026
K ATARI 100K CURVES
K.1 EADREAM
Alinn
Amilarr
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Aster's
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Chopper Command
Crazy Clmbber
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Frooway
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Hero
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ADOK
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<OCK
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Kung Fu Master
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Eye
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-20
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2004
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0.6
10000
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20000
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2004
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Harmony Dream
Figure 11. Training curves of EADream, DreamerV3, and Harmony Hremo on the Atari 100K benchmark. 100K interaction data amounts to 400K frames.
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K.2 EASIMULUS
200
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Battle Zone
Boxing
Breakout
Chopper Command
Crazy Climber
0000
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2004
<box
200K
1OCK
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4D0K
Demon Allack
Freeeway
Gophar
Hero
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Kengaroo
Krull
Mu
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R0CK
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Obart
Road Runner
20
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<OCK
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EASimulus (ours)
Simulus
Figure 12. Training curves of EASimulus and Simulus on the Atari 100K benchmark. 100K interaction data amounts to 400K frames.
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Published as a conference paper at ICLR 2026
L DEEPMIND CONTROL 500K CURVES
Acrobot Swinggu
Caripole Swingup Sparse
Cheetah Run
Finger Turn Easy
1000
500
0
0
600K
500K
1M
500K
1M
500K
M
Finger Tum Hard
Hopper Hop
Hopper Stand
Quadruped Run
1000
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500
BOOK
TM
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5O0K
1M
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1M
S00K
TM
Quadruped Walk
Walker
Mean
Median
750
500
500
500
0
500K
%
500K
500K
500K
EADream (Ours)
TD-MPC2
Figure 13. Training curves of EADream and TD-MPC2 on the challenging tasks from DeepMind Control. 1M frames corresponds to 500K interaction data.
M COMPUTATIONAL RESOURCES
EADream Experiments of EADream on Atari 100K were conducted with NVIDIA V100 32GB GPUs. Training on Atari 100K, with three tasks running on the same GPU in parallel, took about 1.2 days, resulting in an average of 0)4 days per environment (+1 1111 more than DreamerV3). Experiments on DMC were conducted with NVIDIA GeForce RTX 4090 24GB GPUs. Training on DMC, with three tasks running the same GPU in parallel, took 1.8 days, resulting in average of 0.6 days per environment (+13% more than DreamerV3) On V100 GPUs, training baseline Dreamer on DMC-GB2 cost us 2.3 days, while training an EADream model cost 2.6 days.
EASimulus Our experimental evaluations of Simulus were finished on NVIDIA GeForee RTX 4090 24GB GPUs. Training baseline Simulus model Atari 100K and Craftax 1M required approximately 0.7 day and days per task, respectively. Compared to the training cost of our EASimulus (on Atari 100K and Craftax 1M, approximately 0.8 day and days per task, respectively).
N VISUALIZATIONS
N. ATARI 100K
We visualize the predictions of observations by our EAWM compared to pretrained video generation models. Since existing models were not pre-trained with low-resolution images, we resize images to 512 x 512 as inputs for pre-trained video generation models. We tried several pre-trained video generation models (Blattmann et al., 2023; Esser et al., 2024) to generate future frames conditioned on the past frames and proper prompts, among which the best results are selected. As shown in Figure 14 and Figure 15, the pre-trained model often misses the moving patterns of small targets, while our EAWM can make fine-grained predictions of future frames.
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21
31
51
Ground Truth
006
00h
EAWM
Diffusion Model
Figure 14. Comparison of predicted frames for the game Breakout by our EAWM and pretrained diffusion models. Notably, at time = 11, EAWM succeeds in predicting the change of score from to 6 in the upper part of the frame and the color change of the tiny ball. However, the pre-trained Stable Diffusion model even misses information about the tiny ball at time 51.
11
1=
31
[=41
51
Ground Truth
EAWM
Dilfusion Model
Figure 15. Comparison of predicted frames for the game Pong by EAWM and pretrained diffusion models. EAWM succeeds in predicting the change score from 3 to the upper part of the frame at time & = 11 and has more accurate estimation of the moving tiny objects than the pre-trained diffusion model.
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N.2 DMC-GB2
We provide empirical evidence to support that observation prediction is much harder to generalize to unseen environments than event prediction on DMC-GB2 (Almuzairee al., 2024). As shown in Figures 16 to 18, EAWM, DreamerV3 (Hafner et al., 2023), and DyMoDreamer (Zhang et al., 2025) make satisfactory future predictions during training. However, the observation prediction by these models fails in the unseen test environments. We observe that event prediction generalizes well to unseen test environments where the background may be changing continuously. In addition, these results provide a reasonable rationale for the excellent performance of EADream on DMC-GB2.
Ground Truth
BIM
Difference images Imagined images
Imagined events
Figure 16. Predictions in training environments of Cup Catch by EADream at imagination step 9.
Ground Truth
Imagined images
Difference imnges
Figure 17. Predictions in training environments of Cup Catch by DreamerV3 at imagination step 9.
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Published as a conference paper at ICLR 2026
Ground Truth
Imagined images
Difference images
Masked images
Figure 18. Predictions in training environments of Cup Catch by DyMommer at imagination step
9. Given predefined threshold DyMoDreamer defines mask M, for the current observation Og. where each element M,,,. if loo,,,,.e O s.weel > e. Then, masked images are generated via M. o and encoded to obtain dynamic features.
Ground Truth
Imagined events Difference images Imagined images
U
Figure 19, Predictions in unseen test environments of Cup Catch by EADream at imagination step 9.
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Published as a conference paper at ICLR 2026
Ground Truth
Imagined images
Difference images
Figure 20. Predictions in unseen test environments of Cup Catch by DreamerV3 at imagination step
Ground Truth
Imagined images
Difference images
Masked images
Figure 21. Predictions in unseen test environments of Cup Catch by DyMoDreamer at imagination
step 9. Masked images proposed by DyMomrer fail to produce sparse dynamic features in environments where the background is changing.
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Published as a conference paper at ICLR 2026
Ground Truth
Imagined images
Difference images
Imagined events
Figure 22, Predictions in unseen test environments of Cartpole Swingup by EADream imagination step 3.
Ground Truth
Imagined events Difference images Imagined images
Figure 23, Predictions in unseen test environments of Cartpole Swingup by EADream imagination step 33.
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Published as a conference paper at ICLR 2026
Ground Truth
Imagined images
Difference images
Imagined events
Figure 24. Predictions in test environments of Cartpole Swingup by EADream at imagination step 63.
Ground Truth
Imagined events Difference images Imagined images
Figure 25. Predictions in test environments of Cheetah Run by EADream at imagination step 3.
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Published as a conference paper at ICLR 2026
Ground Truth
Imagined images
Imagined events Difference images
Figure 26. Predictions in unseen test environments of Cheetah Run by EADream at imagination step 33.
Ground Truth
Imagined events Difference images Imagined images
Figure 27, Predictions in unseen test environments of Cheetah Run by EADream at imagination step 63.
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Published as a conference paper at ICLR 2026
O USE OF LARGE LANGUAGE MODELS
This paper made limited use of large language models (LLMs) as general-purpose writing and editing assistants. Specifically, GPT-S-thinking was used to help refine the clarity, conciseness, and flow of certain sections and to polish technical descriptions for readability. All research ideas, experiments, analyses, and results presented in this paper were conceived, designed, and executed entirely by the authors without assistance from LLMs. The LLM did not contribute to the novelty of the method, experimental design, or interpretation of results, and was not used to generate figures, code, or data.
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Reviews:
[{"review_id": "0eMLKyroLu", "reviewer": "Reviewer_zrEf", "summary": "This paper proposes Event-Aware World Model (EAWM), a framework that enhances model-based reinforcement learning by predicting events rather than raw observations. The method introduces three key components: an automated event generator that extracts events from multi-modal observations without manual labels, an event predictor that shapes representations through information bottleneck optimization, and a Generic Event Segmentor (GES) that identifies event boundaries to stabilize training.", "questions": "* Why do EASimulus and EADream have different event predictor inputs? EASimulus conditions on $(y_{t-1}, y_t)$ while EADream conditions on $(h_t, \\hat{z}_t, z_t)$. \n* Can the authors provide qualitative imagination results on DMC-GB2? Given that DMC-GB2 tests generalization to visually noisy observations (randomized colors and video backgrounds), it would be valuable to visualize the world model's imagined trajectories and corresponding event predictions. This would help verify whether EAWM truly learns to attend to task-relevant objects while filtering out background distractors, or if the performance gains come from other factors.\n* What is the wall-clock time comparison with baselines?\n\nI am willing to raise my score if the authors can address these concerns.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* Well-motivated approach.\n* The paper demonstrates consistent and substantial improvements across diverse benchmarks.\n* The paper successfully demonstrates applicability across different architectures.", "weaknesses": "* The core idea of integrating dynamics between frames as an additional learning signal for world models has already been explored by DyMoDreamer [1]. Moreover, DyMoDreamer achieves better performance than EADreamer on both Atari 100K and DeepMind Control Vision benchmarks with a simpler method.\n* While the authors claim to learn task-relevant environmental dynamics, the EAWM primarily relies on inter-frame pixel differences. This approach may still filter in many task-irrelevant events.\n* Adding the event detector likely increases the training time for world models. The paper does not report wall-clock training time comparisons or computational cost analysis.\n\n[1] Dymodreamer: World Modeling with Dynamic Modulation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ZDgJh8S045", "reviewer": "Reviewer_z3Wd", "summary": "The paper suggest to extend a typically world model for reinforcement learning (e.g. Dreamer) by an event predictor.  The paper claims that that addition leads to better latent states.  Experiments show that those latent states indeed achieve better performance on various benchmarks.", "questions": "- Eq. (1): $p_i$ doesn't appear in the formula!  when is $p_i$ positive or negative for visual inputs?\n\n- How sensitive is the method to the choice of $C_I$ in Eq. (1)?\n\n- Automated Event Generator:  what are events e.g., in the game of Pong?\n\n- I didn't really understand, what is the point of the \"Generic Event Segmentator\"?  What do you mean with \"reallocate attention from events to raw observations\"?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- well written introduction\n\n- excellent results across different benchmark datasets, e.g. Atari 100K\n\n- the framework can be hooked to existing methods, e.g, Simulus and Dreamer, and consistently improves results\n\n- the additional events are auto-labeled using an \"Automated Event Generator\" (based on Adaptive GMMs)", "weaknesses": "- not much to criticize!", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2DRxHAaK3x", "reviewer": "Reviewer_6jd5", "summary": "The paper adds an \"event-aware\" layer to world models so that agents learn from meaningful changes (events) rather than raw frames alone. It defines events across modalities, predicts them with a dedicated head, and uses a Generic Event Segmentor (GES) to weight losses. Plugging this into DreamerV3 and Simulus yields strong empirical gains across Atari 100K, Craftax, DMC, and DMC-GB2.", "questions": "- The method adds several hyperparameters on top of already hyperparameter-heavy world models. How sensitive are the new hyperparameters in practice, and how difficult was tuning?\n- Please address the clarification requests in the weaknesses above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 3, "strengths": "- The paper proposes a novel extension that can be attached to existing world models.\n- Strong empirical performance across benchmarks.", "weaknesses": "- Event definition in Section 2.2 feels rushed:\n  - Eq. (1): the image intensity $I_t$ is not explicitly introduced in the text.\n  - Line 151: \"direction of the event\" is unclear, does this mean the sign of the brightness change?\n  - For ordinal data, the event is defined as $p_i$. For consistency with visual data it should likely be a tuple e_{t,i}.\n  - Eq. (2): $\\Delta o_i$ is not defined.\n  - This carries into Eqs. (3)-(4): wwhat exactly are the events $e$? In places it seems $p$ might be intended instead of $e$.\n- Discrepancies in Figure 3 vs. Eq. (3):\n  - The sequence model is labeled $F_\\theta$ in the figure but $f_\\theta$ in the equation.\n  - In Eq. (3), the representation model is conditioned on $y_t$, in the figure it is conditioned on $h_t$.\n  - In Eq. (3), the event predictor conditions on $y_{t-1}, y_t$, in the figure it appears to use $y_t, y_{t+1}$.\n  - Why is the observation predictor categorized under \"EA\" rather than \"WM\"?\n- The sentence \"We employ Adaptive Gaussian Mixture Models to automatically detect such events from video\" needs concrete detail. What features are modeled, how thresholds are chosen/updated, and how detections map to the event tuple. This is central to the method but currently vague.\n- The description of the Geneirc Event Segmentor is hard to follow:\n  - How is $\\alpha_t$ computed exactly, do indices $i$ range over pixels or batches? This should be made precise.\n  - Line 272: please define \"event boundaries.\"\n  - The role of GES (as a weighting coefficient for the event/observation losses) emerges only from the equations. The text says \"reallocate attention\" but should explicitly tie to Eqs. (7)-(8).\n  - The function $g$ in Eq. (6) appears without explanation in the main text, only the appendix clarifies it.\n  - From Eq. (6), GES seems independent of $\\theta$. If it has no learned parameters, why is it part of the world model rather than a fixed weighting step?\n- Reported scores show discrepancies vs prior work:\n  - DreamerV3 numbers on Atari 100K are lower than the original paper (mean 1.15 reported here vs 1.25), which makes the gap to EADream's 1.29 smaller than suggested. Please clarify whether these were re-runs and why they differ.\n  - For Simulus, the reported mean and median (1.61 and 0.74) differ from the Simulus paper (1.65 and 0.98). Please explain the deviation.\n- Ablations are unclear:\n  - \"No Event Predictor\": What exactly is disabled? If event prediction is off, shoudn't the model reduce to the original world model? Why do \"EADream w/o Event Predictor\" and DreamerV3 differ? Is this related to the RSSM-OP change, but if so, why is there the same effect for Simulus?\n  - \"No GES\": If GES is off, is $\\omega$ also set to $0$? If so, consider an additional ablation where GES is on but $\\omega = 0$ to disentangle effects.\n  - \"Without Observation Prediction\": RSSM-OP seems to matter a lot. An ablation \"Dreamer + RSSM-OP\" would isolate its contribution.\n- Minor: Missing citations to Craftax and DMC in Section 4.1.\n\nI'm willing to raise my score if the presentation is improved and these points are addressed, as the method and results look very promising. My current score is low because, in its present form, the paper should not be accepted due to several presentation issues, but if these are fixed I would lean toward acceptance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)