Title: GenRL: Multimodal-foundation world models for generalization in embodied agents
Venue: NeurIPS 2024
Accepted: yes
Keywords: world models

foundations models

reinforcement learning

multitask generalization
Abstract: Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundation vision-language models (VLMs) generally require fine-tuning or other adaptations to be adopted in embodied contexts, due to the significant domain gap. However, the lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications. In this work, we overcome these problems by presenting multimodal-foundation world models, able to connect and align the representation of foundation VLMs with the latent space of generative world models for RL, without any language annotations. The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain’s dynamics, and learn the corresponding behaviors in imagination.
As assessed through large-scale multi-task benchmarking in locomotion and manipulation domains, GenRL enables multi-task generalization from language and visual prompts. Furthermore, by introducing a data-free policy learning strategy, our approach lays the groundwork for foundational policy learning using generative world models. 
Website, code and data: https://mazpie.github.io/genrl/

GenRL: Multimodal-foundation world models for generalization in embodied agents

Abstract

Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundation vision-language models (VLMs) generally require fine-tuning or other adaptations to be adopted in embodied contexts, due to the significant domain gap. However, the lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications. In this work, we overcome these problems by presenting multimodal-foundation world models, able to connect and align the representation of foundation VLMs with the latent space of generative world models for RL, without any language annotations. The resulting agent learning framework, GenRL, allows one to specify tasks through vision and/or language prompts, ground them in the embodied domain's dynamics, and learn the corresponding behaviors in imagination. As assessed through large-scale multi-task benchmarking in locomotion and manipulation domains, GenRL enables multi-task generalization from language and visual prompts. Furthermore, by introducing a data-free policy learning strategy, our approach lays the groundwork for foundational policy learning using generative world models.

Website, code and data:

mazpie.github.io/genrl

1 Introduction

Foundation models are large pre-trained models endowed with extensive knowledge of the world, which can be readily adapted for a given task [44]. These models have demonstrated extraordinary generalization capabilities in a wide range of vision [28, 45, 67] and language tasks [43, 19, 53, 11]. As we aim to extend this paradigm to embodied applications, where agents physically interact with objects and other agents in their environment, we require generalist agents that are capable of reasoning about these interactions and executing action sequences within these settings [61].

Reinforcement learning (RL) allows agents to learn complex behaviors from visual and/or proprioceptive inputs [18, 26, 27] by maximizing a specified reward function. Scaling up RL to multiple tasks and embodied environments remains challenging as designing reward functions is a complicated process, requiring expert knowledge and prone to errors which can lead to undesired behaviors [1]. Recent work has proposed the adoption of visual-language models (VLMs) to specify rewards for visual environments using language [4, 48, 39], e.g. using the similarity score computed by CLIP [45] between an agent's input images and text prompts. However, these approaches mostly require fine-tuning of the VLM [38], otherwise, they tend to work reliably only in a few visual settings [48].

In most RL settings, we lack multimodal data to train or fine-tune domain-specific foundation models, due to the costs of labelling agents' interactions and/or due to the intrinsic unsuitability of some embodied contexts to be converted into language. For instance, in robotics, it's non-trivial to convert a language description of a task to the agent's actions which are hardware-level controls, such as motor currents or joint torques. These difficulties make it hard to scale current techniques to large-scale generalization settings, leaving open the question:

How does one effectively leverage foundation models for generalization in embodied domains?

In this work, we present GenRL, a novel approach requiring no language annotations that allows training agents to solve multiple tasks from visual or language prompts. GenRL learns multimodalfoundation world models (MFWMs), where the joint embedding space of a foundation video-language model [57] is connected and aligned with the representation of a generative world model for RL [23], using only vision data. The MFWM allows the specification of tasks by grounding language or visual prompts into the embodied domain's dynamics. Then, we introduce an RL objective that enables learning to accomplish the specified tasks in imagination [24], by matching the prompts in latent space.

Compared to previous work in world models and VLMs for RL, one emergent property of GenRL is the possibility to generalize to new tasks in a completely data-free manner. After training the MFWM, it possesses both strong priors over the dynamics of the environment, and large-scale multimodal knowledge. This combination enables the agent to interpret a large variety of task specifications and learn the corresponding behaviors. Thus, analogously to foundation models for vision and language, GenRL allows generalization to new tasks without additional data and lays the groundwork for foundation models in embodied RL domains [44].

2 Preliminaries and background

Additional related works can be found in Appendix A.

Problem setting. The agent receives from the environment observations x ∈ X and interacts with it through actions a ∈ A . In this work, we focus on visual reinforcement learning, so observations are images of the environment. The objective of the agent is to accomplish a certain task τ , which can be specified either in the observation space x τ , e.g. through images or videos, or in language space y τ , where Y represents the space of all possible sentences. Crucially, compared to a standard RL setting, we do not assume that a reward signal is available to solve the task. When a reward function exists, it is instead used to evaluate the agent's performance.

Generative world models for RL. In model-based RL, the optimization of the agent's actions is done efficiently, by rolling out and scoring imaginary trajectories using a (learned) model of the environment's dynamics. In recent years, this paradigm has grown successful thanks to the adoption of generative world models, which learn latent dynamics by self-predicting the agent's inputs [23]. World models have shown impressive performance in vision-based environments [24],

(a) Connecting and aligning (Section 3.2)

(b) Learning task behavior (Section 3.3)

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improving our ability to solve complex and open-ended tasks [26]. Generative world models have been successfully extended to many applications, such as exploration [51], skill learning [42], solving long-term memory tasks [50], and robotics [58, 16].

Foundations models for RL. Large language models (LLMs) have been used for specifying behaviors using language [41, 29, 56, 59], but this generally assumes the availability of a textual interface with the environment or that observations and/or actions can be translated to the language domain. The adoption of vision-language models (VLMs) reduces these assumptions, as it allows the evaluation of behaviors in the visual space. However. this approach has yet to show robust performance, as it generally requires fine-tuning of the VLM [4, 15], prompt hacking techniques [9] or visual modifications to the environment [4].

Vision-language generative modelling. Given the large success of image-language generative models [49], recent efforts in the community have focused on replicating and extending such success to the video domain, where the temporal dimension introduces new challenges, such as temporal consistency and increased computational costs [30, 3]. Video generative models are similar to world models for RL, with the difference that generation models outputs are typically not conditioned on actions, but rather conditioned on language [30] or on nothing at all (i.e. an unconditional model).

3 GenRL

3.1 World models for RL

GenRL learns a task-agnostic world model representation by modelling the sequential dynamics of the environment in a compact discrete latent space S [24, 26]. Latent states s ∈ S are sampled from independent categorical distributions. The gradients for training the model are propagated through the sampling process with straight-through estimation [5].

The world model is made of the following components:

trained with the loss:

where p ϕ ( s t | s t -1 , a t -1 ) is a shorthand for p ϕ ( s t | f ϕ ( s t -1 , a t -1 , h t -1 )) . The sequence model is implemented as a linear GRU cell [8]. Differently from recurrent state space models (RSSM; [25]),

for our framework, encoder and decoder models are not conditioned on the information present in the sequence model. This ensures that the latent states only contain information about a single observation, while temporal information is stored in the hidden state of the sequence model. Given the simpler encoder-decoder strategy of our model, the encoder can be seen as a probabilistic visual tokenizer, which is grounded in the target embodied environment [64].

3.2 Multimodal-foundation world models

Multimodal VLMs are large pre-trained models that have the following components:

where x t : t + k is a sequence of visual observations and y is a text prompt. For video-language models, k is generally a constant number of frames (e.g. k ∈ { 4 , 8 , 16 } frames). Image-language models are a special case where k = 1 as the vision embedder takes a single frame as an input. For our implementation, we adopt the InternVideo2 video-language model [57] (with k =8).

To connect the representation of the multimodal foundation VLM with the world model latent space, we instantiate two modules: a latent connector and a representation aligner :

where sg ( · ) indicates to stop gradients propagating.

The connector learns to predict the latent states of the world model from embeddings in the VLM's representation space. The connector's objective consists of minimizing the KL divergence between its predictions and the world model's encoder distribution. While more expressive architectures, such as transformers [55] or state-space models [21] could be adopted, we opt for a simpler GRU-based architecture for video modelling. This way, we keep the method simple and the architecture of the connector is symmetric with respect to the world model's components.

Aligning multimodal representations. The connector learns to map visual embeddings from the pretrained VLM to latent states of the world model. When learning the connector from visual embeddings e ( v ) , we assume it can generalize to the (theoretical) corresponding language embedding e ( l ) if the angle θ between the two embeddings is small enough, as shown in Fig. 3a. This can be expressed as cos θ > c or θ < arccos c , with c a small positive constant [68].

Multimodal VLMs trained with contrastive learning exhibit a multimodality gap [34], where the spherical embeddings of different modalities are not aligned. Given a dataset of vision-language data, this projective function can be learned. However, in embodied domains vision-language data is typically unavailable. Thus, we have to find a way to align the representations using no language annotations .

Previous methods inject the noise into the vision embeddings during training [66, 68]. This leads to the situation shown in Figure 3b, where c grows larger with the noise. This allows language embeddings to be close enough to their visual counterparts.

In our work, we instead learn an aligner network, which maps points surrounding e ( v ) closer to e ( v ) . As represented in Fig. 3c, this way, c is unaltered but the aligner will map e ( l ) close enough to e ( v ) . Since we use noise to sample points around e ( v ) the aligner model can be trained using vision-only data and thus, no language annotations.

Figure 3: When training the connector on (a) the VLM's representation we can address the multimodality gap in multiple ways: (b) prior works adopt noise during the training of the connector, (c) we adopt an aligner network that learns to map points in proximity of the visual embedding close the corresponding embedding.

e (l)

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The aligner allows us to train a noise-free connector, which has two main advantages: (i) it yields higher prediction accuracy for visual embedding inputs while maintaining a similar alignment for language embedding inputs; and (ii) it is more flexible; it's easier to re-train/adapt for different noise levels, as it only requires re-training the aligner module, and its use can be avoided if unnecessary.

3.3 Specifying and learning tasks in imagination

World models can be used to imagine trajectories in latent space, using the sequential and dynamics models. This allows us to train behavior policies in a model-based RL fashion [24]. Given a task specified through a visual or language prompt, our MFWM can generate the corresponding latent states by turning the embedder's output, e task, into sequences of latent states s t : t + k (decoded examples are shown in Figure 1). The objective of the policy model π θ is then to match the goals specified by the user by performing trajectory matching.

The trajectory matching problem can be solved as a divergence minimization problem [13], between the distribution of the states visited by the policy π θ and the trajectory generated using the alignerconnector networks from the user-specified prompt:

The KL divergence is a natural candidate for the distance function [13]. However, in practice, we found that using the cosine distance between linear projections of the latent states notably speeds up learning and enhances stability. We can then turn the objective in Eq. 2 into a reward for RL:

where g ϕ represents the first linear layer of the world model's decoder. We train an actor-critic model to maximize this reward and achieve the tasks specified by the user [26]. Additional implementation details are provided in Appendix B.

Temporal alignment. One issue with trajectory matching is that it assumes that the distribution of states visited by the agent starts from the same state as the target distribution. However, the initial state generated by the connector may differ from the initial state where the policy is currently in. For example, consider the Stickman agent on the right side of Figure 1. If the agent is lying on the ground and tasked to run, the number of steps to get up and reach running states may surpass the temporal span recognized by the VLM (e.g. in our case 8 frames), causing disalignment in the reward.

To address this initial condition alignment issue, we propose a best matching trajectory technique, inspired by best path decoding in speech recognition [20]. Our technique involves two steps:

Wecompare the first b states of the target trajectory with b states obtained from the trajectories imagined by the agent by sliding along the time axis. This allows one to find at which timestep t a the trajectories are best aligned (the comparison provides the highest reward).

We align the temporal sequences in the two possible contexts: (a) if a state from the agent sequence comes before t a , the reward uses the target sequence's initial state; and (b) if the state comes k steps after t a , it's compared to the s t + k state from the target sequence.

In all experiments, we fix b = 8 (number of frames of the VLM we use [57]), which we found to strike a good compromise between comparing only the initial state ( b = 1 ) and performing no alignment ( b = imagination horizon ) . An ablation study can be found in Appendix E.

4 Experiments

Overall, we employ a set of 4 locomotion environments (Walker, Cheetah, Quadruped, and a newly introduced Stickman environment) [54] and one manipulation environment (Kitchen) [22], for a total of 35 tasks where the agent is trained without rewards, using only visual or language prompts. Details about the datasets, tasks, and prompts used can be found in the Appendix C.

4.1 Offline RL

In offline RL, the objective of the agent is to learn to extract a certain task behavior from a given fixed dataset [33]. The performance of the agent generally depends on its ability to 'retrieve' the correct behaviors in the dataset and interpolate among them. Popular techniques for offline RL include off-policy RL methods, such as TD3 [18], advantage-weighted behavior cloning, such as IQL [31], and behavior-regularized approaches, such as CQL [32] or TD3+BC [17].

We aim to assess the multi-task capabilities of different approaches for designing rewards using VLMs. We collected large datasets for each of the domains evaluated, containing a mix of structured data (i.e. the replay buffer of an agent [26] learning to perform some tasks) and unstructured data (i.e. exploration data collected using [51]). The datasets contain no reward information and no text annotations of the trajectories. The rewards for training for a given task must be inferred by the agent, i.e. using the cosine similarity between observations and the given prompt or, in the case of GenRL, using our reward formulation (Eq. 3).

We compare GenRL to two main categories of approaches:

Image-language rewards : following [48], the cosine similarity between the embedding for the language prompt and the embedding for the agent's visual observation is used as a reward. For the VLM, we adopt the SigLIP-B [65] model as it's reported to have superior performance than the original CLIP [45].

Video-language rewards : similar to the image-language rewards, with the difference that the vision embedding is computed from a video of the history of the last k frames, as done in [15]. For the VLM, we use the InternVideo2 model [57], the same used for GenRL.

The evaluation compares GenRL to various offline RL methods from the literature, including IQL, TD3+BC, and TD3. We also introduce a model-based baseline, WM-CLIP . This baseline is the antithesis of GenRL as, rather than learning a connector and an aligner, it learns a 'reversed connector". This module learns to predict VLM embeddings from the world model states (GenRL does the opposite). This makes it possible to compute rewards in imagination in a similar way to the modelfree baselines, by computing the cosine similarity between the visual embeddings predicted from imagined states and the task's language embeddings.

All methods are trained for 500k gradient steps, and evaluated on 20 episodes. For each task, modelfree agents require training the agent from scratch, including the visual encoder, actor, and critic networks on the entire dataset. Model-based agents require training the model once for each domain and then training an actor-critic for each task. Other training and baseline details are reported in Appendix D.

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Language-to-action in-distribution. We want to verify whether the methods can retrieve the task behaviors that are certainly present in the training data, when specifying the task only through language. We present results in Table 1, with episodic rewards rescaled so that 0 represents the performance of a random agent, while 1 represents the performance of an expert agent.

GenRL excels in overall performance across all domains and tasks, outperforming other methods particularly in dynamic tasks like walking and running in the quadruped and cheetah domains. However, in some static tasks of the kitchen domain, other methods occasionally outperform GenRL. This can be explained by the fact that the target sequences that GenRL infers from the prompt are often slightly in motion, even in static cases. To address this, we could set the target sequence length to 1 for static prompts, but we opted to maintain the method's simplicity and generality, acknowledging this as a minor limitation.

As expected, video-language rewards tend to perform better than image-language rewards for dynamic tasks. The less conservative approach, TD3, performs better than the other model-free baselines in most tasks, similarly to what is shown in [62]. The model-based baseline's performance, WM-CLIP-V, is the closest to GenRL's.

Language-to-action generalization. To assess multi-task generalization, we defined a set of tasks not included in the training data. Although we don't anticipate agents matching the performance of expert models, higher scores in this benchmark help gauge the generalization abilities of different methods. We averaged the performance across various tasks for each domain and summarized the findings in Figure 4, with detailed task results in Appendix K.

Overall, we observe a similar trend as for the in-distribution results. GenRL significantly outperforms all model-free approaches, especially in the quadruped and cheetah domains, where the performance is close to the specialized agents' performance. Both for image-language (-I in the Figure) and video-language (-V in the Figure) more conservative approaches, such as IQL and TD3+BC tend to perform worse. This could be associated with the fact that imitating segments of trajectories is less likely to lead to high-rewarding trajectories, as the tasks are not present in the training data.

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Video-to-action. While language strongly simplifies the specification of a task, in some cases providing visual examples of the task might be easier. Similarly as for language prompts, GenRL allows grounding visual prompts (short videos) into the embodied domain's dynamics and then learning of the corresponding behaviors.

In Figure 5, we provide behavior learning results from video prompts. The tasks included are of static and dynamic nature and span across 4 different domains. Visualizations of the videos used as prompts are available on the project website, where we also present a set of 'grounded videos" generated by the model using the prompts (see snapshots at the top of Fig. 5). These can be obtained by inferring the latent targets corresponding to the vision prompts (left images, in the Figure) and then using the decoder model to decode reconstructed images (right images, in the Figure).

The results show a similar trend to the language prompts experiments and the performance when using video prompts is aligned to the language-to-action performance, for the same tasks. In general, we found it interesting that the VLM allows us to generalize to very different visual styles (drawings, realistic, AI-generated), very different camera viewpoints (quadruped, microwave), and different morphologies (cheetah tasks).

Summary. The experiments presented allow us to establish more clearly the main ingredients that contribute to the stronger performance of GenRL: (i) the video-language model helps in dynamic tasks, (ii) model-based algorithms lead to higher performance, (iii) the connection-alignment system presented generally outperforms the 'reversed" way of connecting the two representations.

4.2 Data-free policy learning

In the previous section, we evaluated several approaches for designing reward using foundation VLMs. Clearly, model-free RL approaches require continuous access to a dataset, to train the actor-critic and generalize across new tasks. Model-based RL can learn the actor-critic in imagination. However, in previous work [26, 24], imagining sequences for learning behaviors first requires processing actual data sequences. The data is used to initialize the dynamics model, and obtain latent states that represent the starting states to rollout the policy in imagination. Furthermore, in order to learn new tasks, reward-labelled data is necessary to learn a reward model, which provides rewards to the agent during the task learning process.

Foundation models [44] are generally trained on enormous datasets in order to generalize to new tasks. The datasets used for the model pretraining are not necessary for the downstream applications, and sometimes these datasets are not even publicly available [43, 19]. In this section, we aim to establish a new paradigm for foundation models in RL, which follows the same principle of foundation models for vision and language. We call this paradigm data-free policy learning and we define it as the ability to generalize to new tasks, after pre-training, by learning a policy completely in imagination, with no access to data (not even to the pre-training dataset).

GenRL enables data-free policy learning thanks to two main reasons: the agent learns a task-agnostic MFWM on a large varied dataset during pre-training, and the MFWM enables the possibility of specifying tasks directly in latent space, without requiring any data. Thus, in order to learn behaviors in imagination, the agent can: (i) sample random latent states in the world model's representation,

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(ii) rollout sequences in imagination, following the policy's actions, and (iii) compute rewards, using the targets obtained by processing the given prompts with the connector-aligner networks.

In Figure 6, we provide a diagram that further clarifies the differences between training GenRL in an offline and in a data-free policy learning fashion. Then, we present results that compare datafree policy learning with offline RL baselines, as discussed in Section 4.1. While data-free policy learning shows a slight decrease in overall performance, its performance remains close to the original GenRL's performance and still outperforms other approaches. In Appendix K, we further show that the difference in performance is minimal across most domains, and data-free policy learning even performs better in the kitchen domain.

By employing data-free learning, after pre-training, agents can master new tasks without data. By requiring no CPU-GPU memory transfers of the data, data-free policy learning also reduces the training time of the policy, often allowing convergence within only 30 minutes of training. As we scale up foundation models for behavior learning, the ability to learn data-free will become crucial. Although very large datasets will be employed to train future foundation models, GenRL adapts well without direct access to original data, offering flexibility where data may be proprietary, licensed or unavailable.

4.3 Analysis of the training data distribution

As demonstrated in Sections 4.1 and 4.2, after training on a large dataset, a GenRL agent can adapt to multiple new tasks without additional data. The nature of the training data, detailed in Appendix C, combines exploration and task-specific data. Thus, we ask ourselves what subsets of the data are the most important ones for GenRL's training.

To identify critical data types for GenRL, we trained different MFWMs on various dataset subsets. Then, we employ data-free behavior learning to train task behaviors for all tasks. We present an analysis over subsets of the walker dataset in Figure 7.

The results confirm that a diverse data distribution is crucial for task success, with the best performance achieved by using the complete dataset, followed by the varied exploration data. Task-specific data effectiveness depends on task complexity, for instance, 'run data' proves more useful and generalizable than 'walk data' or 'stand data' across tasks. Crucially, 'stand data', which shows minimal variation, limits learning for a general agent but can still manage simpler tasks like 'lying down' and 'sitting on knees' as detailed in Appendix K.

Moving forward with training foundation models in RL, it will be essential to develop methods that extract multiple behaviors from unstructured data and accurately handle complex behaviors from large datasets. Thus, the ability of GenRL to primarily leverage unstructured data is a significant advantage for scalability.

5 Discussion

We introduced GenRL, a world-model based approach for grounding vision-language prompts into embodied domains and learning the corresponding behaviors in imagination. The multimodalfoundation world models of GenRL can be trained using unimodal data, overcoming the lack of multimodal data in embodied RL domains. The data-free behavior learning capacity of GenRL lays the groundwork for foundation models in RL that can generalize to new tasks without any data.

A framework for behavior generation. A common challenge with using LLMs and VLMs involves the need for prompt tuning to achieve specific tasks. As GenRL relies on a foundation VLM, similar to previous approaches [4, 48] it is not immune from this issue. However, GenRL uniquely allows for the visualization of targets obtained from specific prompts. By decoding the latent targets, using the MFWMdecoder, we can visualize the interpreted prompt before training the corresponding behavior. This enables a much more explainable framework, which allows fast iteration for prompt tuning, compared to previous (model-free) approaches which often require training the agent to identify which behaviors are rewarded given a certain prompt.

Limitations. Despite its strengths, GenRL presents some limitations, largely due to inherent weaknesses in its components. From the VLMs, GenRL inherits the issue related to the multimodality gap [34, 66] and the reliance on prompt tuning. We proposed a connection-alignment mechanism to mitigate the former. For the latter, we presented an explainable framework, which facilitates prompt tuning by allowing decoding of the latent targets corresponding to the prompts. From the world model, GenRL inherits a dependency on reconstructions, which offers advantages such as explainability but also drawbacks, such as failure modes with complex observations. We further investigate this limitation in Appendix I and present other potential limitations in Appendix J.

Future work. As we strive to develop foundation models for generalist embodied agents, our framework opens up numerous research opportunities. One such possibility is to learn multiple behaviors and have another module, e.g. an LLM, compose them to solve long-horizon tasks. Another promising area of research is investigating the temporal flexibility of the GenRL framework. We witnessed that for static tasks, greater temporal awareness could enhance performance. This concept could also apply to actions that extend beyond the time comprehension of the VLM. Developing general solutions to these challenges could lead to significant advancements in the framework.

Acknowledgments and Disclosure of Funding

Pietro Mazzaglia is funded by a Ph.D. grant of the Flanders Research Foundation (FWO). This research was supported by a Mitacs Accelerate Grant.

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A Extended Related Work

[Linked to Section 2]

World models. Recent research has focused on the question of how to learn world models from large-scale video datasets [37, 60]. In [6], they leverage a latent action representation, but their work is mostly focussed on 2D platform videogames or simple robotic actions. In [14], they use frame-by-frame video prediction as a way to provide rewards for RL. DynaLang [36] studies the incorporation of language prediction as part of the world model, to train multimodal world models also from datasets without actions or rewards. The representation in DynaLang is shared in the world model between vision and language, while for GenRL, the world model representation is trained on vision-only data and connected-aligned to the multimodal foundation representation.

Foundation models for actions. Few cases of foundation models for embodied domains have been developed. Notable mentions are GATO [47], a large-scale behavior cloning agent, trained on 604 tasks. VPT [2] a large-scale model trained on Minecraft data, using human-expert labeled trajectories. The model learns strong behavioral priors by behavior cloning which can be fine-tuned using RL. STEVE-1 [35] connects VPT's behavioral prior with the MineCLIP model representation [15], using the unCLIP approach [46]. RT-X [12] are large-scale transformer models trained on expert robotics dataset, sharing a common action space (end-effector pose) across different embodiments.

B Implementation details

[Linked to Section 3]

Actor-critic. Rewards can be maximized over time in imagination in a RL fashion, using actor-critic models of the form:

For the actor-critic, we follow the implementation advances proposed in DreamerV3 [26] (version 1 of the paper, dated January 2023), such as using a two-hot distribution for learning the critic network and scaling returns in the actor loss.

When computing the reward r GenRL, we use the mode of the distribution for the target s task t +1 ∼ p ψ ( s t +1 | e task ) to improve stability.

Hyperparameters. For the hyperparameters, we follow DreamerV3 [26] (version 1 of the paper, dated January 2023). Differences from the default hyperparameters or model size choices are illustrated in Table 2. For instance, a main difference is that we use difference batch sizes/lengths for training the MFWM and the actor-critic as these two stages are now independent from each other.

The connector network uses the same hyperparameters and architecture as the sequential dynamics of the world model. The aligner network employs a small U-Net, with a bottleneck that is half the size of the embedding representation. The embedding representation is 768-dimensional. Further details can be found in our accompanying code implementation.

C Tasks

[Linked to Section 4]

Stickman environment. The Stickman environment is based on the Walker environment from the dm_control suite. We designed the Stickman environment to explore tasks that require upper body limbs (e.g. boxing, doing a handstand) without the complexity of training a humanoid (which requires a significantly larger amount of data to be solved [63]). The number of joints is increased by 4: 2 joints per arm, one is for the shoulder, the other for the elbow. The total number of joints is 10. The action space is normalized to be in [-1,1] as all dm_control tasks. The robot also presents a head, to resemble a humanoid.

Prompts and scores. We present the list of tasks employed, along with the language prompts used for specifying the task, in Table 3. For the newly introduced tasks, the goal can be easily inferred by reading the task's name or its prompt. For the 'flipping' tasks, we consider flips both in the forward direction and backward direction, as the VLM struggles to distinguish directions. The reward functions used to evaluate the agent's score can be found in our open-source code.

The prompts we use have been fine-tuned for the InternVideo2 model [57]. However, we found that they mostly improved performance for the SigLIP model too [65]. One common observation is that these models are generally biased towards human actions. Thus, specifying the embodiment in the prompt is sometimes helpful, e.g. 'spider running fast' or 'running like a quadruped'. Another observation is that for some behaviors the agent can produce very different styles, e.g. the agent can be walking in a slow or fast way, or in a more or less composed manner. Specifying words like 'fast' or 'clean' helps clarifying what kind of behavior is expected.

D Experiments settings

[Linked to Section 4]

Baselines. In order to implement performant model-free offline RL baselines we adopt the findings of [52] and [7], adopting larger deeper networks and layer normalization. Inputs are 64x64x3 RGB images. We use a frame stack of 3. The encoder architecture is adapted from the DrQ-v2 encoder [63]. We did find augmentations on the images, e.g. random shifts, to hurt performance.

The WM-CLIP baseline learns a 'reversed connector" from the world model representation to the VLM representation (GenRL does the opposite). The 'reversed connector", given the latent state corresponding to a certain observation, predicts the corresponding embedding. Formally:

After training the reversed connector, visual embeddings can be inferred from latent states. For policy learning, rewards are computed using the cosine similarity between embeddings inferred from imagined latent states and the prompts' embedding.

The reversed connector is implemented as a 4-layer MLP, with hidden size 1024. For fair comparison, we adopt the same world model for WM-CLIP and GenRL. For WM-CLIP we pre-train the additional reversed connector, while for GenRL the connector and aligner.

Offline RL. For each task, training model-free agents (IQL, TD3, TD3+BC) requires re-training the full agent (visual encoder, actor, critic) on the entire dataset, from scratch, while training model-based agents (GenRL, WM-CLIP) requires training the model once for each domain and then training an actor-critic for each task. Moreover, for training the actor-critic in GenRL, we only use 50k gradient steps, as the policy converges significantly faster than for the other methods.

Datasets composition. We present the datasets' composition in Table 4.

Compute resources. We use a cluster of V100 with 16GB of VRAM for all our experiments. To enable efficient training, image and video-CLIP embeddings are computed in advance and stored with the datasets. Training the MFWMfor 500k gradient steps takes ∼ 5 days. After pre-training the MFWM, training the actor-critic for a prompt for 50k gradient steps takes less than 5 hours. In data-free mode, it takes less than 3 hours. In both cases, convergence normally arrives after 10k gradient steps, but we keep training. Model-free baselines take around 7 hours to train for 500k gradient steps.

On a single GPU, model-free RL is faster to train for a small number of runs. GenRL starts becoming advantageous when using the world model for training for more than 60 runs (which is often the case, considering the number of runs = N seeds x M tasks per domain). When adopting the data-free policy learning strategy, GenRL doesn't rely on the dataset at all. This halves the time required for training, as there are no data transfers between the CPU (where the dataset is normally loaded) and the GPU for training.

E Temporal alignment ablation

[Linked to Section 3]

In Figure 8, we investigate the impact of our best-matching trajectory strategy for temporally aligning imaginary latent states to target states for GenRL's reward computation.

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F Aligner model ablation study

[Linked to Section 3]

To establish the importance of the aligner network, in Table 5 we report the results of additional ablations:

GenRL - no aligner: this is an ablation of GenRL where the the language prompt's embedding is directly fed into the connector, rather than processing it first with the aligner;

TD3-V and WM-CLIP-V + aligner: for these ablations, we first process the language prompt's embedding using GenRL's pre-trained aligner. Then, we use it to compute the cosine similarity for the reward function, as for the original baselines.

We can observe that: i) the aligner mechanism is crucial in GenRL's functioning. ii) processing the language embedding in the reward function of the WM-CLIP-V and TD3-V baselines changes performance on some tasks (performance per domain varies). However, using the aligner provides no advantage overall.

We believe the aligner is very important in GenRL because its output, the processed language embedding, is fed to another network, the connector. If the language embeddings were not processed by the aligner, they would have been too different from the embeddings used to train the connector, which are the visual embeddings.

Instead, for the baselines, we process the language embedding with the aligner and then use it to compute a similarity score with the visual embeddings. This overall renders very similar performance to no aligner processing, hinting that the aligner network doesn't improve the cosine similarity signal. At the same time, this also suggests that the aligner network doesn't hurt the generality of the VLM's embeddings, as the cosine similarity after processing the embedding provides a similarly useful signal as before processing.

G Comparison with LIV

[Linked to Section 4]

We also tested all baselines with the LIV's representation [40] in the Kitchen tasks. We used LIV's open-source code to download and instantiate the model. Note that the available model is the general pre-trained model, not the one fine-tuned for the Kitchen environment.

LIV's results confirm the original paper's claims (see Appendix G4) that the representation does not work well for vision-language rewards without fine-tuning on domain-specific vision-language pairs (which are unavailable in our settings, as we use no language annotations).

H Extended Discussion on Data-free Policy Learning

[Linked to Section 4]

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Offline RL methods (Fig. 9a) learn from offline datasets of observations, actions and rewards.

Offline RL methods (Fig. 9b), combined with VLMs, can learn to perform tasks zero-shot from new prompts, but they need to sample observations and actions from the dataset for computing rewards and for policy learning.

GenRL (Fig. 9c) needs to sample (sequences of) observations from the dataset, to infer the initial latent states for learning in imagination. Afterwards, rewards can be computed on the imagined latent sequences, enabling policy learning.

Data-free GenRL (Fig. 9d), samples the initial latent states internally by combining: (i) random samples of the latent space, (ii) randomly sampled embeddings, which are mapped to 'actual embeddings" using the aligner, and turned into latent states, by the connector. Thus, policy learning requires no data sampling at all.

Initial states distribution. Uniform sampling from the latent space of the world model often results in meaningless latent states. Additionally, the sequential dynamics model of the MFWM, using a GRU, requires some 'warmup' steps to discern dynamic environmental attributes, such as velocities.

To address these issues we perform two operations. First, we combine uniformly sampled states from the discrete latent spaces with states generated by randomly sampling the connector model, as sequences generated by the connector tend to have a more coherent structure than random uniform samples. Second, we perform a rollout of five steps using a mix of actions from the trained policy and random actions. This leads to a varied distribution of states, containing dynamic information, which we use as the initial states for the learning in imagination process.

I Scaling to complex observations

[Linked to Section 5]

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Generalist embodied agents should be able to scale to open-ended learning settings. Using GenRL, we explored this by training an agent in the Minecraft environment using a small dataset collected by a DreamerV3 agent [26]. Note that GenRL is also trained using similar experimental settings as DreamerV3, e.g. setting up the environment in the same way. The primary challenge we found was the model's difficulty in reconstructing complex observations in this open-ended environment.

Reconstructing complex observations is a common issue with world models [10]. To overcome this limitation, while keeping the method unaltered, we attempted to scale up the number of parameters of the MFWM. Qualitative reconstruction results are presented in Figure 10. We observe that the agent is able to identify different biomes from language, even with the smaller size of the model. However, the reconstructions are significantly blurrier compared to the other environments we analyzed. When using a larger model, the reconstructions gain some details but the results still highlight the difficulty of the model in providing accurate targets from prompts.

While this might not be an issue for simple high-level tasks, e.g. 'navigate to a beach', inferring unclear targets might make it difficult to perform more precise actions, e.g. 'attack a zombie'. Future research should aim to address this issue, for instance, by improving our simple GRU-based architecture, leveraging transformers or diffusion models to improve the quality of the representation [30, 3].

J Additional limitations

[Linked to Section 5]

Pre-training data requirements. As we developed our framework, we observed that, in order to solve more complex tasks, the agent requires some expert data/demonstration of the complex behavior. We observed and analyzed this aspect in the experiments of Section 4.3. We believe this limitation is, to some extent, inevitable, as data-driven AI agents need to observe complex behaviors during training in order to be able to replicate them.

In this work, we used some exploration data, obtained using an exploration agent (Plan2Explore), and some task-specific data, collected using an expert RL agent (DreamerV3). Alternatively, one could investigate the adoption of a small set of demonstrations.

Precise manipulation skills. Adding additional tasks to the Kitchen environment, for testing multi-task generalization, showed to be harder than for the other domains. This is due to the difficulties of exploring meaningful behaviors in manipulation environments.

We are able to use GenRL to retrieve the four tasks present in the dataset and we are generally able to achieve similar tasks, such as 'reach the microwave". From decoding the language prompts, we also see that GenRL can sensibly decode prompts such as 'moving to the left" or 'staying still", or more 'unusual' prompts such as

'swan pose" (where the robot arm would imitate the pose of a swan). While these prompts show that the system works well in this environment, none of these tasks would be particularly useful.

When we prompt the system with more interesting tasks that are out of the distribution, the agent generally struggles. For instance, if we ask to open the double door cabinet on the top left, the agent may reach for it but will not open it. The reason behind this is that it probably never opened that cabinet in the training dataset and thus it's impossible to reproduce such behavior in an offline manner. The same issues are most likely present with all the baselines as well, as they all use the same dataset and foundation models for training.

For the locomotion domains, instead, the exploration data is varied enough that new tasks, outside of the tasks present in the training dataset, are enabled through the exploration data/

K Detailed results

[Linked to Section 4]

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NeurIPS Paper Checklist

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors propose learning a pixel-based reconstructive world model, and then separately learn networks to convert the representations of a pretrained VLM into the learned world model latent space.  By using a VLM trained via contrastive alignment, this essentially enables the projection of both image as well as text inputs into the latent space of the world model, and therefore simple similarity can be used to provide rewards for downstream policy learning.\n\nSTRENGTHS:\nThis reviewer is a supporter of the idea of unifying the representation spaces of a large-scale pretrained VLM and that of a world model.  This author appreciates the benefits: matching behavior of a world model with natural language can enable text-conditioned generalization.  The preliminary experiments show promise.\n\n**Originality**: This work appears decently original.\n\n**Quality**: This works quality is acceptable.\n\n**Clarity**: The clarity of the work is acceptable, the core ideas are communicated clearly.  However, there are lots of open questions surrounding this work that could be elaborated upon further.\n\n**Significance**: This work appears to be decently significant, as a preliminary investigation in this space.\n\nWEAKNESSES:\nChief amongst the weaknesses of this work is the limited environments applied to, and also the limited baselines (essentially, the only existing work the authors compare against is VLM-RM).  The authors can consider comparing against other forms of text-conditioned policy learning, such as LIV for the kitchen setting, or Text2Reward and similar approaches for the general case.  It also seems a strange setup to normalize in-between expert and random, and report results in this way.  This reviewer is unaware of prior work that performs evaluations in this way.  What is the rationale behind this evaluation strategy compared to what is used in prior work?\n\nDetails about certain components of the model and how they are implemented are sparse.  For example, is the aligner a video generative model (text-to-video model)?  How is it implemented?\n\nIt is a bit dissatisfying to rely on a corrupted version of vision as a language embedding.  It seems strange that the aligner should on one hand be learning to bring language embeddings meaningfully across modalities to the image/video space, which the authors motivate is necessary because of the multimodality gap.  However, the authors then treat language embeddings as a noisy corruption of a video embedding - so essentially the training objective for the aligner is essentially a denoising?  And rather than bridging a modality gap, the aligner is essentially a denoiser?\n\nWhy do we not learn the reverse direction, where we optimize a world model's latent space that projects into existing VLM space?  This design decision is not elaborated upon, but seems more intuitive to this reviewer.\n\nFrom the video demonstrations, on the associated project website, it is rather unclear what is happening.  Are Behavior Retrieval videos from expert policies in an offline dataset that are matched with a particular text prompt/input video?  What are those text prompts/input videos?  It's not clear what the retrieval setup is.  For Multitask Generalization, it is also not obvious what the corresponding prompts are.  Furthermore, the results for multitask generalization do not seem smooth and natural, despite being simplistic DM_Control environments (especially the case for their proposed simplified Stickman environment) and they are missing Kitchen environments.  In the end, it appears that their method is still good as a retrieval technique (retrieving already-achieved expert behaviors in \"Behavior retrieval\") due to the underlying VLM, and is decent at reconstructing video prompts, but still suffers in terms of learning coherent policies (e.g. what is visualized in \"Multitask generalization\"), which is ultimately what is of interest.\n\nFor the video prompts that are decoded, it appears as if almost all of them are rather stationary (with the exception of the cheetah/dog example and the human dancing example) - they collapse to a stationary goal pose.  Perhaps this is because the clips are so short (8 frames) that essentially it boils down to pose-matching.  It is not obvious that this is that beneficial in supervising motion; so why does this improve upon static image supervision?  Indeed, many of the results that are shown learned by the policy are rather stationary and do not have much movement (most are just jitters around a stationary pose).  It then begs the question how this approach improves upon just a static goal supervision.  However, the authors simultaneously find that in static tasks other methods outperform the authors' approach.\n\nThis reviewer pushes back on the term \"data-free RL\", as there still needs data (and interaction data) to learn their method.  This is a very confusing terminology, and honestly the generalization comes from the large-scale pretrained VLM - it would be more appropriate to reuse the terminology of zero-shot reward models or zero-shot policy learning used in prior works across alignment methods (vision-language models are zero-shot reward models for reinforcement learning, [Rocamonde, '23]) and diffusion (text-aware diffusion for policy learning, [Luo, '24]).\n\nThis reviewer really enjoys the work but believes there are many open questions that warrant further explanation.  Furthermore, the evaluation suite (environments) and comparison suite (benchmarks) is rather weak.  The idea is indeed neat, but the execution leaves much to be desired, and therefore this reviewer believes the work is of borderline quality.\n\nQUESTIONS:\nWhy Stickman instead of Humanoid?  Humanoid has been able to be solved with pixel-based world models in the past (Appendix A of Dreamerv2).  What are the specifications of Stickman and with what criteria was it designed?\n\nWhy did the authors use a simple GRU?  Why was a more advanced world model not used, like RSSMs?  Was this tested or ablated over?\n\nWhy were there no multi-task generalization experiments performed for Meta-World kitchen?\n\nWould the authors consider Text2Reward and other approaches that learn a reward function as data-free RL, as there do not need additional data to generalize to learning new policies?  Alternatively, the data-free RL paradigm sounds like zero-shot generalization for policy learning, which is already offered by VLM-RM and other similar works.\n\nWhy did the authors choose to generate the video demonstrations synthetically, rather than use actual natural video clips?  Would the performance not be better when using natural videos which are more in line with what the base VLM was trained on?\n\nHow are the rewards computed using temporal alignment?  Essentially, are only the rewards for the most-aligned segments across the target trajectory and the agent used as rewards, and for all other timesteps a 0 reward is provided?  This computation seems rather expensive for long-horizon trajectories.\n\nLIMITATIONS:\nThe limitations section seems acceptable.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper looks at a method for leveraging foundation multimodal models for learning world models in RL. They do so by aligning the latent space of a video language model with that of a generative model that can be used for learning in imagination. This is done by training connector-and-aligner networks . The rewards for a task can then be derived by measuring the cosine sim between representations of the states visited by a policy and the states generated by the connector aligner network when it is conditioned on a language-based task prompt. A policy can be optimised to maximise this alignment based reward.\n\nSTRENGTHS:\nTransferring foundation model knowledge to improve policy learning is an open problem of interest to the community. \n\nThe paper provides a successful recipe for aligning a foundation model with the world model for a specific domain that we want to do policy learning in. \n\nThe paper is written well.\n\nI'm currently being conservative in giving a borderline accept score, since some aspects of the method are not clear to me (I have addressed this in my questions below) - but I will be happy to raise my score after engaging with the authors once they have addressed these questions.\n\nWEAKNESSES:\n1. I would have expected that simple tasks with clearly distinguishable static end states (such as standing) should have worked equally well with CLIP rewards, however the table shows a big difference between the proposed method and the image-language reward baselines even on those tasks, which leads me to think that the baselines may be missing out on some component that the proposed method has. What could be missing, or is this intuition wrong? \n2. The generations in Fig 6a are actually not accurate at all - many of the poses don’t correspond to the humanoid pose if you look closely and would actually optimize learning to strike the wrong pose if a policy is trained with it.\n\nQUESTIONS:\n1. Why is setting b=imagination horizon the same as doing no alignment? (Line 160)\n2. I’m not completely sure how you train the aligner-connector network: is it done by 1) using images collected from the downstream domain (in this case from the mujoco sim), 2) getting their VLM visual embedding and their world model encoder embedding and aligning those? As for the text part, is this done by corrupting the VLM visual embedding (to approximate the language embedding) and aligning it again with the world model encoder?  What is the policy used to collect the data and resulting data distribution? I understand that Fig 5 is somehow related to this question but this could be made clearer. For eg. which task’s policy is chosen to collect the data to train the MWFM for the results in the main table (how is this policy related to the task being evaluated)? \n3. The discussion around Figure 5 is not very clear to me - how do we infer that “’run’ data proves more generalizable than  ’walk’ or ’stand’ data across tasks“ - the figures suggest that training on ‘stand’ led to the highest rewards for downstream tasks\n4. “This can be explained by  the fact that the target sequences that GenRL infers from the prompt are often slightly in motion“ - could you explain why that would be the case (it inferring the closest matching state as one that is in motion)?\n\nLIMITATIONS:\nThe paper includes a brief discussion on limitations of their method.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes to combine a DreamerV3-style world model with a pretrained vision language model (VLM). By training two small adaptors to align the latent space of the VLM with that of the world model, the aligned representations from the VLM can be used as a reward signal to train agents in the world model.\n\nThe training process consists of two main parts. \n\n1) There is a large offline dataset needed in the environments of interest (prompt and trajectories of states and actions), generated by expert RL agents and a random policy. This trains the world model and the adaptors. Each environment (domain) uses a separate world model.\n\n2) Actor-critic agents are trained purely within this world model’s imagination, a separate policy for each task. The paper shows these agents outperform standard model-free offline RL methods trained on only the large offline dataset. It also shows some effectiveness at generalizing to new tasks within an environment, specified with a new text prompt.\n\nSTRENGTHS:\n- The core idea of the paper is very nice. There is a lot of interest from the community in working out how to get value from the broad general knowledge locked away in LLMs and VLMs, into RL agents. This paper offers a novel way to attack this – to my knowledge world models have not been used in this context before. \n\n- The results are not dazzling, but they indicate the approach works and it outperforms standard (though perhaps weak) offline-RL baselines. Section 4.2 shows promise in generalizing to knew text prompts in an existing environment.\n\nWEAKNESSES:\nMy main criticism of the paper is that the narrative oversells what the core work actually supports. I detail examples below. Overall I’d suggest either presenting the work that has been done comprehensively in a more reserved manner, or adding the required work to support the broader claims and experiments. Either way, I think changes would be large enough to require a resubmission.  I’m disappointed to not be able to give the paper a higher score as I liked the main idea. \n- The capability of the model to condition on visual goals is presented as a main functionality of the model – featuring in the first figure, the abstract, and throughout the paper. But the only evidence to support this is a very brief and qualitative experiment (Figure 6a). Everything else is conditioned on text. I am of the opinion that conditioning on visuals would likely work, but the paper must present good evidence to support this.\n- Several aspects of the title ‘Multimodal foundation world models for generalist embodied agents’ are misleading. 1) Only one modality is really tested (as in prior point). 2) ‘Foundation world models’ suggested I'd see a single very general world model. But in Appendix D is an important detail -- each environment learns a separate world model, so they are only general or foundational within a specific Mujoco embodiment. This kind of detail is important and should be honestly discussed in the main paper. 3) A ‘generalist agent’ is referred to, but every agent in the paper only performs a single specialist task, there is nothing general about the agent’s themselves.\n- The method is reported as needing ‘no language annotations’ (line 42). This is not true. The large offline dataset requires text prompts accompanying each trajectory.\n- The paper claims to be ‘the first large-scale study of multitask generalization from language in RL’ (line 165), but I can think of others. Language table is the first that comes to mind. \n- One of the motivations for the work is that reward functions can be hard to specify, while language is a more natural form. However, the large offline dataset is generated by using multiple expert agents which need reward functions.\n- ‘Data free RL’ is suggested as a new paradigm for foundation models in RL. I’d argue that this is simply know as zero-shot generalization to most in the community.\n- Main experiments are presented in Table 1. Whilst the offline-RL methods are one comparison point, I’m not sure how comparable they are, since they are all model-free while GenRL is model based. Are there any model-based variants that would be easily considered as baselines? The differences are reflected in the different compute times required – GenRL takes 5 days for world model training +5 hours per policy, while the baselines take 7 hours per policy. This seems like an unfair comparison, especially to withhold the detail to the appendix.\n- Results in Minecraft are briefly mentioned in Section 5. But so few details are given that I am lost as to what it is showing. This should either be removed or full details added. \n- The paper presents a new stickman environment. But details are sparse. The authors have failed to correctly identify this in Checklist Section 13.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nFine.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 1, "DATE": "2024-07-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper wants to leverage the large-scale pre-training of foundation models trained on internet data to train a world model for embodied agents that generalizes across tasks and domains. This is done by training a world model in the standard way, but in addition training aligner and connector networks that (1) map language embeddings to video embeddings and (2) map video embeddings to world model latent states. At inference time, this allows conditioning the world model on a task language prompt and then training in imagination to learn policies.\n\nSTRENGTHS:\n- On the website, the reconstruction results from language and video are nice and quite unexpected (I'm unsure why the aligner and connector networks are able to generalize to new prompts) \n- The problem the paper is trying to solve is relevant, especially given the mismatch in data availability between embodied and vision / language settings\n\nWEAKNESSES:\n- The main claim of the paper is strong generalization performance, leveraging the internet scale pre-training of video-language models. The bottleneck is the generalization ability of the networks which map embeddings from the video-language model to the world model latent states, and on the quality of the world model itself. I don't see why the aligner and connector should generalize.\n- Given the main claim, I would like stronger baselines / ablations in the generalization and data-free settings. Currently, there are no baselines in the data-free case which makes it impossible to assess how well the method generalizes.  \n- Many of the experimental details are unclear in the paper (please see my questions). I encourage the authors to explain these better in the rebuttal and camera-ready, and also provide some intuition for why their method is better than the baselines.  \n- In the single task, offline RL case, all the baselines are model-free, whereas the proposed method utilizes a model. I would have liked to see at least one model-based baseline to confirm that the improvement is because of the better reward signal and not because of the model-based optimization.\n- In the single task, offline RL case, reward is computed by looking at the similarity between the representations of the task prompt and the image / video. In the case of the base lines, these representations are fixed (eg. CLIP / Intern2Video representations), whereas for the proposed method these are taken from the last layer of the model learnt on the data itself. This is also reflected in the compute budget - the model takes 5 days to train (in addition to the 5 hours of training in imagination).\n\nQUESTIONS:\n- What is the value of $k$ (number of frames predicted by the connector)? What duration does this correspond to? What happens if the task is longer than this duration?\n-  Just to confirm, in the offline RL evaluation, first the world model is trained on the offline data (only for that particular domain) and then the policy is trained in imagination? In that case, why is there a difference in the time taken for the actor-critic to converge in the data-free setting (see line 524)\n-  In the single task, offline RL case, is the aligner trained with only as many language prompts as the tasks in that domain? If that is the case, it would be trained to reconstruct $e^{(v)}$ corresponding to many different videos in the offline dataset, some of which might contain suboptimal trajectories which have nothing to do with the language prompt. How can we expect the aligner to learn anything useful in this case?\n- If the aligner is trained only on a few language prompts, how is it able to generalize to new tasks?\n- What exactly is the multi-task generalization setting? In this evaluation, does the method get access to offline data from the OOD task? If yes, how is it used to train the policy? If no, how are the model-free baselines trained in this setting?\n\nLIMITATIONS:\nYes, the authors adequately assessed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-18"}