Original: Intra-agent speech permits zero-shot task acquisition
Revised: Intra-agent speech permits zero-shot task acquisition

Original: Anonymous Author(s)

Original: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Human language learners are exposed to a trickle of informative, context-sensitivelanguage, but a flood of raw sensory data.
Revised: Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data.

Original: Through both social language use andinternal processes of rehearsal and practice, language learners are able to buildhigh-level, semantic representations that explain their perceptions.
Revised: Through both social language use and internal processes of rehearsal and practice, language learners are able to build high-level, semantic representations that explain their perceptions.

Original: Here, we takeinspiration from such processes of “inner speech” in humans (Vygotsky, 1934) tobetter understand the role of intra-agent speech in embodied behaviour.
Revised: Here, we take inspiration from such processes of “inner speech” in humans (Vygotsky, 1934) to better understand the role of intra-agent speech in embodied behaviour.

Original: First, weformally pose intra-agent speech as a semi-supervised problem and develop twoalgorithms that enable visually grounded captioning with little labeled languagedata.
Revised: First, we formally pose intra-agent speech as a semi-supervised problem and develop two algorithms that enable visually grounded captioning with little labeled language data.

Original: We then experimentally compute scaling curves over different amountsof labeled data and compare the data efficiency against a supervised learningbaseline.
Revised: We then experimentally compute scaling curves over different amounts of labeled data and compare the data efficiency against a supervised learning baseline.

Original: Finally, we incorporate intra-agent speech into an embodied, mobilemanipulator agent operating in a 3D virtual world, and show that with as fewas 150 additional image captions, intra-agent speech endows the agent with theability to manipulate and answer questions about a new object without any relatedtask-directed experience (zero-shot).
Revised: Finally, we incorporate intra-agent speech into an embodied, mobile manipulator agent operating in a 3D virtual world, and show that with as few as 150 additional image captions, intra-agent speech endows the agent with the ability to manipulate and answer questions about a new object without any related task-directed experience (zero-shot).

Original: Taken together, our experiments suggest thatmodelling intra-agent speech is effective in enabling embodied agents to learn newtasks efficiently and without direct interaction experience.
Revised: Taken together, our experiments suggest that modelling intra-agent speech is effective in enabling embodied agents to learn new tasks efficiently and without direct interaction experience.

Original:  Introduction
Revised:  Introduction

Original: Contemporary language models learn from troves of text comprising billions, and sometimes trillionsof tokens.
Revised: Contemporary language models learn from troves of text comprising billions, and sometimes trillions of tokens.

Original: This is not the same situation faced by embodied language learners, such as human children,whose raw sensory data vastly exceeds their social-linguistic experiences.
Revised: This is not the same situation faced by embodied language learners, such as human children, [Figure] whose raw sensory data vastly exceeds their social-linguistic experiences.

Original: Psychologists have longnoted the existence of non-social, intra-personal dialogue—such as inner speech (silent) or privatespeech (aloud)—which provides additional linguistic experiences beyond those encountered in socialsettings.
Revised: Psychologists have long noted the existence of non-social, intra-personal dialogue—such as inner speech (silent) or private speech (aloud)—which provides additional linguistic experiences beyond those encountered in social settings.

Original: Indeed, one possible reason for intra-personal speech may be to support the development ofsocial speech [1–5], though the inverse causal relation may also be possible [1, 6].
Revised: Indeed, one possible reason for intra-personal speech may be to support the development of social speech [1–5], though the inverse causal relation may also be possible [1, 6].

Original: Intra-personal speech may also affect broader aspects of our cognition: the process wherein we privately speak about our perceptions subsequently impacts how we attend to, remember, or manipulatethe objects of our perception [7–12].
Revised: Intra-personal speech may also affect broader aspects of our cognition: the process wherein we privately speak about our perceptions subsequently impacts how we attend to, remember, or manipulate the objects of our perception [7–12].

Original: For example, our short-term memories tend to commit errorsfor items that are phonetically but not visually or semantically similar [8, 9], and “self-talk” affectstask performance on executive tasks both in children [11, 12] and in adults [7], and has been widelyused to improve athletic performance [10].
Revised: For example, our short-term memories tend to commit errors for items that are phonetically but not visually or semantically similar [8, 9], and “self-talk” affects task performance on executive tasks both in children [11, 12] and in adults [7], and has been widely used to improve athletic performance [10].

Original: Inspired by these ideas, here we ask if we can leverage intra-agent speech as a tool for artificialintelligence research.
Revised: Inspired by these ideas, here we ask if we can leverage intra-agent speech as a tool for artificial intelligence research.

Original: Specifically, we ask the following questions: First, is it possible to modelintra-agent speech as a machine learning problem, enabling us to make up for a paucity of languagedata when given access to a large amount of unlabeled data?
Revised: Specifically, we ask the following questions: First, is it possible to model intra-agent speech as a machine learning problem, enabling us to make up for a paucity of language data when given access to a large amount of unlabeled data?

Original: Second, can mechanisms for learningintra-agent speech impact the subsequent behavior of an embodied agent?
Revised: Second, can mechanisms for learning intra-agent speech impact the subsequent behavior of an embodied agent?

Original: Regarding the first, we formally structure intra-agent speech as a semi-supervised problem.
Revised: Regarding the first, we formally structure intra-agent speech as a semi-supervised problem.

Original: Inparticular, we model intra-agent speech samples as variational latent variables representing images,[Figure] and use this perspective to devise two algorithms enabling visual captioning given a paucity ofdirectly labeled data.
Revised: In particular, we model intra-agent speech samples as variational latent variables representing images, and use this perspective to devise two algorithms enabling visual captioning given a paucity of directly labeled data.

Original: Regarding the second, we go on to show how embedding such algorithms in anembodied agent not only permits the capacity to speak about any given perception (that is, to caption ,which is precisely what is being optimized for) but rather also permits zero-shot behaviors involvingnew objects that were never directly implicated in any task-directed experience (see Figure 1).
Revised: Regarding the second, we go on to show how embedding such algorithms in an embodied agent not only permits the capacity to speak about any given perception (that is, to caption , which is precisely what is being optimized for) but rather also permits zero-shot behaviors involving new objects that were never directly implicated in any task-directed experience (see Figure 1).

Original:  Approach
Revised:  Approach

Original: At a high level, we structured intra-agent speech as the production of language generated from inputs(which in our case manifests as captions of images), with two additional concerns: First, there willbe a preponderance of unlabeled data (i.e., pure images) relative to labeled data (i.e., images withassociated captions) in our captioning dataset, much like the situations faced by human childrenin nature and semi-supervised learning in machine learning.
Revised: At a high level, we structured intra-agent speech as the production of language generated from inputs (which in our case manifests as captions of images), with two additional concerns: First, there will be a preponderance of unlabeled data (i.e., pure images) relative to labeled data (i.e., images with associated captions) in our captioning dataset, much like the situations faced by human children in nature and semi-supervised learning in machine learning.

Original: Second, the linguistic representationseventually produced will provide auxiliary supervision for an embodied, acting agent so that theymay influence interactive behavior. 
Revised: Second, the linguistic representations eventually produced will provide auxiliary supervision for an embodied, acting agent so that they may influence interactive behavior. [Figure]

Original: In humans, the learning of external and internal speech, the rehearsal of inner speech, and thepractice of tasks can develop concurrently.
Revised: In humans, the learning of external and internal speech, the rehearsal of inner speech, and the practice of tasks can develop concurrently.

Original: In the first stage, we pretrain a captioning model bysemi-supervised learning, using a small amount of labeled language data and the large amount ofagent’s experience as unlabeled data.
Revised: In the first stage, we pretrain a captioning model by semi-supervised learning, using a small amount of labeled language data and the large amount of agent’s experience as unlabeled data.

Original: This model is able to commentate about visual observations – acritical function of “inner speech”.
Revised: This model is able to commentate about visual observations – a critical function of “inner speech”.

Original: This model is then used to commentate (via what we have named“intra-agent speech”) on further experiences to shape representations as the agent goes on to learnspecific tasks, and we show comparisons of the agent’s performance with and without this intra-agentspeech process.
Revised: This model is then used to commentate (via what we have named “intra-agent speech”) on further experiences to shape representations as the agent goes on to learn specific tasks, and we show comparisons of the agent’s performance with and without this intra-agent speech process.

Original: A diagram of the semi-supervised learning approach to intra-agent speech and its role in acquiringbehaviors is shown in Figure 2.
Revised: A diagram of the semi-supervised learning approach to intra-agent speech and its role in acquiring behaviors is shown in Figure 2.

Original: 2.1 Methods for Semi-supervised Language Learning
Revised: 2.1 Methods for Semi-supervised Language Learning

Original: Our data D are composed of a dataset D p of paired data { x , y } and a dataset D u of unpaired data{ x } .
Revised: Our data D are composed of a dataset D p of paired data { x , y } and a dataset D u of unpaired data { x } .

Original: In our interpretation, y corresponds to language (a caption) describing the image x .
Revised: In our interpretation, y corresponds to language (a caption) describing the image x .

Original: Whenobserving unpaired data, we treat y as a latent variable that must be inferred to explain the observeddata [13].
Revised: When observing unpaired data, we treat y as a latent variable that must be inferred to explain the observed data [13].

Original: We develop two variants, one generative and one contrastive, of the semi-supervisedlanguage learning algorithm in what follows.
Revised: We develop two variants, one generative and one contrastive, of the semi-supervised language learning algorithm in what follows.

Original: [Figure]

Original: 2.1.1 Generative variant
Revised: 2.1.1 Generative variant

Original: The preponderance of our data is unlabeled.
Revised: The preponderance of our data is unlabeled.

Original: To model these data we can maximise a variational lowerbound on the log marginal distribution, log p θ ( x ) , corresponding to posterior inference.
Revised: To model these data we can maximise a variational lower bound on the log marginal distribution, log p θ ( x ) , corresponding to posterior inference.

Original: We willuse a learned approximation to the true posterior distribution, q ω ( y | x ) .
Revised: We will use a learned approximation to the true posterior distribution, q ω ( y | x ) .

Original: We call this approximatedistribution an “image-conditional language encoder” (or sometimes, simply, caption model ) sinceits functional role is to convert images into a discrete latent code that is, ideally, a semanticallymeaningful language caption:(cid:88)
Revised: We call this approximate distribution an “image-conditional language encoder” (or sometimes, simply, caption model ) since its functional role is to convert images into a discrete latent code that is, ideally, a semantically meaningful language caption:

Original: [Equation]
Revised: [Equation]

Original: In Equation 1, the space of latent codes (captions) is discrete and vast, complicating optimization.
Revised: In Equation 1, the space of latent codes (captions) is discrete and vast, complicating optimization.

Original: However, we can form a stochastic approximation by sampling from q ω ( y | x ) to estimate a gradient.
Revised: However, we can form a stochastic approximation by sampling from q ω ( y | x ) to estimate a gradient.

Original: [Equation]
Revised: [Equation]

Original: For simplicity, we pretrain the prior p ϕ ( y ) as a language model that learns on all of our caption data,and fix it during this optimisation phase.
Revised: For simplicity, we pretrain the prior p ϕ ( y ) as a language model that learns on all of our caption data, and fix it during this optimisation phase.

Original: Thus, optimising J u corresponds to an entropy-regularised
Revised: Thus, optimising J u corresponds to an entropy-regularised

Original: RL problem [14–16].
Revised: RL problem [14–16].

Original: The encoder q ω ( y | x ) serves as a policy that achieves reward for matching thelanguage model prior while enabling the language-conditional decoder to reconstruct the image.
Revised: The encoder q ω ( y | x ) serves as a policy that achieves reward for matching the language model prior while enabling the language-conditional decoder to reconstruct the image.

Original: While this reward pressures the image-conditional language encoder to produce linguistically meaningful latent codes, we can also exert additional pressure by using a small number of imageswith associated captions (“labeled data”), and leverage supervised learning to learn log p θ ( x |y ) , log p ϕ ( y ) , log q ω ( y | x ) :
Revised: While this reward pressures the image-conditional language encoder to produce linguistically meaningful latent codes, we can also exert additional pressure by using a small number of images with associated captions (“labeled data”), and leverage supervised learning to learn log p θ ( x | y ) , log p ϕ ( y ) , log q ω ( y | x ) :

Original: [Equation]
Revised: [Equation]

Original: Over the combined paired and unpaired datasets, our complete objective is to maximise J =
Revised: Over the combined paired and unpaired datasets, our complete objective is to maximise J =

Original: J p + J u .
Revised: J p + J u .

Original: 2.1.2 Contrastive variant
Revised: 2.1.2 Contrastive variant

Original: The language-conditional image decoder p θ ( x | y ) is a potentially complicated term to model.
Revised: The language-conditional image decoder p θ ( x | y ) is a potentially complicated term to model.

Original: To understand if this term represents a liability for the approach and to generate an algorithmwith potentially complementary strengths, we also developed a contrastive, energy-based approach, amounting to multi-class classification, to approximate it [17–19].
Revised: To understand if this term represents a liability for the approach and to generate an algorithm with potentially complementary strengths, we also developed a contrastive, energy-based approach, amounting to multi-class classification, to approximate it [17–19].

Original: If we have a multi-class classifierthat discriminates if a batch element y j is paired to a batch element x j , and we express its crossentropy loss for the correct index c
Revised: If we have a multi-class classifier that discriminates if a batch element y j is paired to a batch element x j , and we express its crossentropy loss for the correct index c

Original: = j as L ( { x b } Bb =1 , y j , c = j ) , then we can show that thegenerative reconstruction loss log p θ ( x j | y j ) is proportional to L ( { x b } Bb =1 , y j , c = j ) up to constantfactors with respect to y (Appendix A.1).
Revised: = j as L ( { x b } Bb =1 , y j , c = j ) , then we can show that the generative reconstruction loss log p θ ( x j | y j ) is proportional to L ( { x b } Bb =1 , y j , c = j ) up to constant factors with respect to y (Appendix A.1).

Original: Thus, we can substitute L ( { x b } Bb =1 , y j , c = j ) for batchelement j , log p θ ( x j | y j ) , in Equation 2, and the gradients with respect to ω remain the same inexpectation.
Revised: Thus, we can substitute L ( { x b } Bb =1 , y j , c = j ) for batch element j , log p θ ( x j | y j ) , in Equation 2, and the gradients with respect to ω remain the same in expectation.

Original: We represented the softmax in the classifier as
Revised: We represented the softmax in the classifier as

Original: [Equation]
Revised: [Equation]

Revised: Bb

Original:  , where f and g are networks thatcompress the image into a vector and the caption into a vector, respectively.
Revised: [Equation] x b [Equation] g ( y j ) , where f and g are networks that compress the image into a vector and the caption into a vector, respectively.

Original: To train this classifier,we adopted a similar approach to recent models for visual language representation learning [20, 21]by minimizing the sum of two losses, one for matching each image to its paired caption in the batch,and one for matching each caption to its paired image:
Revised: To train this classifier, we adopted a similar approach to recent models for visual language representation learning [20, 21] by minimizing the sum of two losses, one for matching each image to its paired caption in the batch, and one for matching each caption to its paired image:

Original: [Equation]
Revised: [Equation]

Original: The networks f and g are trained at the same time as the rest of the model, and samples fromq ω ( y j | x j ) are included as positive examples for the classifier along with the original paired data.
Revised: The networks f and g are trained at the same time as the rest of the model, and samples from q ω ( y j | x j ) are included as positive examples for the classifier along with the original paired data.

Original: 2.1.3 Intra-agent speech architecture and optimization
Revised: 2.1.3 Intra-agent speech architecture and optimization

Original: Image-conditional language encoder.
Revised: Image-conditional language encoder.

Original: The image-conditional language encoder received inputimages with resolution 96 × 72 .
Revised: The image-conditional language encoder received input images with resolution 96 × 72 .

Original: We used the ResNet architecture described in [22], with strides(1 , 1 , 2 , 2) , 3 × 3 kernel size, and channel sizes (64 , 64 , 128 , 256) for a total of 20 layers.
Revised: We used the ResNet architecture described in [22], with strides (1 , 1 , 2 , 2) , 3 × 3 kernel size, and channel sizes (64 , 64 , 128 , 256) for a total of 20 layers.

Original: Languagewas produced by a 4 -layer causal transformer with 256 embedding size and 4 attention heads [23],which attended to the 432 hyper-pixel vectors generated from the ResNet, and produced a sequenceof logits corresponding to a 4 , 000 token vocabulary.
Revised: Language was produced by a 4 -layer causal transformer with 256 embedding size and 4 attention heads [23], which attended to the 432 hyper-pixel vectors generated from the ResNet, and produced a sequence of logits corresponding to a 4 , 000 token vocabulary.

Original: Language targets were encoded with a SentencePiece byte-pair encoder [24] trained using language data from [25].
Revised: Language targets were encoded with a SentencePiece byte-pair encoder [24] trained using language data from [25].

Original: Generative semi-supervised model.
Revised: Generative semi-supervised model.

Original: The language prior comprised a separate transformer pretrained on all the caption labels in the training data (and hence, did not have image inputs).
Revised: The language prior comprised a separate transformer pretrained on all the caption labels in the training data (and hence, did not have image inputs).

Original: For theimage decoder, we first pre-trained a VQ-VAE [26] on all unlabeled images to define the VQ-VAE’scodebook, compressing each image into 432 tokens with a vocabulary of 512 .
Revised: For the image decoder, we first pre-trained a VQ-VAE [26] on all unlabeled images to define the VQ-VAE’s codebook, compressing each image into 432 tokens with a vocabulary of 512 .

Original: We then used an8-layer transformer with 512 embedding size with causal masking to model the VQ-VAE tokensautoregressively.
Revised: We then used an 8-layer transformer with 512 embedding size with causal masking to model the VQ-VAE tokens autoregressively.

Original: The conditioning captions were tokenized and embedded as in the image-conditionallanguage encoder, and then processed by a distinct transformer with 4 layers, 256 embedding size,and 4 heads, whose output was then cross-attended by the image decoder to produce a reconstruction.
Revised: The conditioning captions were tokenized and embedded as in the image-conditional language encoder, and then processed by a distinct transformer with 4 layers, 256 embedding size, and 4 heads, whose output was then cross-attended by the image decoder to produce a reconstruction.

Original: Contrastive semi-supervised model.
Revised: Contrastive semi-supervised model.

Original: For the contrastive model, images were first encoded using a
Revised: For the contrastive model, images were first encoded using a

Original: ResNet with the same architecture as the image-conditional language encoder, and globally meanpooled into one vector with dimension 1024 .
Revised: ResNet with the same architecture as the image-conditional language encoder, and globally meanpooled into one vector with dimension 1024 .

Original: Language was first encoded using a transformer withthe same 4-layer architecture described above, and then flattened and compressed with a 2-layer MLPwith dimensions (2048 , 1024) .
Revised: Language was first encoded using a transformer with the same 4-layer architecture described above, and then flattened and compressed with a 2-layer MLP with dimensions (2048 , 1024) .

Original: Optimization.
Revised: Optimization.

Original: We used V-MPO [16] to optimize q ω ( y | x ) .
Revised: We used V-MPO [16] to optimize q ω ( y | x ) .

Original: The loss was optimized by the Adamoptimizer [27] with β 1 = 0 .
Revised: The loss was optimized by the Adam optimizer [27] with β 1 = 0 .

Original: 9 , β 2 = 0 .
Revised: 9 , β 2 = 0 .

Original: 999 and a learning rate of 2 × 10 − 4 with early stopping atthe lowest log-likelihood over captions in validation data.
Revised: 999 and a learning rate of 2 × 10 − 4 with early stopping at the lowest log-likelihood over captions in validation data.

Original: We trained all models with a batch sizeof 128 except for the contrastive classifier model, which also received 2 , 048 unlabeled images perbatch, giving a total batch size of 2 , 176 .
Revised: We trained all models with a batch size of 128 except for the contrastive classifier model, which also received 2 , 048 unlabeled images per batch, giving a total batch size of 2 , 176 .

Original: We trained our models using Tensor Processing Units(TPUv3)
Revised: We trained our models using Tensor Processing Units (TPUv3) [28].

Original: In all experiments the early stopping criteria was reached within 150 K optimizationsteps.
Revised: In all experiments the early stopping criteria was reached within 150 K optimization steps.

Original: 2.2 Agent training
Revised: 2.2 Agent training

Original: The agent was trained by behavioral cloning on a large corpus of human interactions in the Playhouseenvironment [22].
Revised: The agent was trained by behavioral cloning on a large corpus of human interactions in the Playhouse environment [22].

Original: Briefly, it comprises a 3D simulated environment wherein two embodied avatarsinteract with natural language to cooperatively accomplish tasks involving object manipulation,navigation, and question answering.
Revised: Briefly, it comprises a 3D simulated environment wherein two embodied avatars interact with natural language to cooperatively accomplish tasks involving object manipulation, navigation, and question answering.

Original: In addition to receiving visual observations and producing motoractions, agents in this environment also receive language inputs and produce language outputs.
Revised: In addition to receiving visual observations and producing motor actions, agents in this environment also receive language inputs and produce language outputs.

Original: Thisallows them to answer questions, participate in dialogue, ask for clarifications, and so on.
Revised: This allows them to answer questions, participate in dialogue, ask for clarifications, and so on.

Original: Our approach to training the linguistic representations of the agent therefore has two steps.
Revised: Our approach to training the linguistic representations of the agent therefore has two steps.

Original: First, byusing semi-supervised language learning, we can amplify the impact of sparse language annotationsprovided for a small number of images in learning the intra-agent speech module.
Revised: First, by using semi-supervised language learning, we can amplify the impact of sparse language annotations provided for a small number of images in learning the intra-agent speech module.

Original: In turn, using thistrained module, we are able to sample captions at high frequency as the agent acts, providing theagent with dense language supervision.
Revised: In turn, using this trained module, we are able to sample captions at high frequency as the agent acts, providing the agent with dense language supervision.

Original: Therefore, while the agent trains using behavioral cloning,we can sample captions from the pretrained intra-agent speech module, and provide these as eitherauxiliary inputs or targets to the agent so that they may influence the agent parameters optimized toproduce embodied behaviors.
Revised: Therefore, while the agent trains using behavioral cloning, we can sample captions from the pretrained intra-agent speech module, and provide these as either auxiliary inputs or targets to the agent so that they may influence the agent parameters optimized to produce embodied behaviors.

Original: For our first loss – the caption loss – captions arising as intra-agent speech samples were provided astargets for the agent’s language output policy:
Revised: For our first loss – the caption loss – captions arising as intra-agent speech samples were provided as targets for the agent’s language output policy:

Original: [Equation]
Revised: [Equation]

Original: where π l is the language action policy of the agent, y c is the caption sample, o represents all visualand text observations, and K represents the maximum unroll length.
Revised: where π l is the language action policy of the agent, y c is the caption sample, o represents all visual and text observations, and K represents the maximum unroll length.

Original: To allow the agent to distinguishwhether it was being tasked with emitting a caption of its current observation, or whether it was beingtasked with emitting language for the purposes of interactive behavior (see [22]), the input of thelanguage policy is summed with a learnable embedding of the indicator variable representing whetherthe target is captioning or language output from demonstration.
Revised: To allow the agent to distinguish whether it was being tasked with emitting a caption of its current observation, or whether it was being tasked with emitting language for the purposes of interactive behavior (see [22]), the input of the language policy is summed with a learnable embedding of the indicator variable representing whether the target is captioning or language output from demonstration.

Original: While the caption loss trains the agent to speak about what it sees, the agent’s language encoding (vialinguistic inputs) remains untrained.
Revised: While the caption loss trains the agent to speak about what it sees, the agent’s language encoding (via linguistic inputs) remains untrained.

Original: To this end we introduced a second loss: the caption-matchingloss.
Revised: To this end we introduced a second loss: the caption-matching loss.

Original: In the caption-matching loss, the agent is required to predict whether a given caption, provided asinput, matches the observed image.
Revised: In the caption-matching loss, the agent is required to predict whether a given caption, provided as input, matches the observed image.

Original: Negative samples (i.e., captions that do not match the observation)are captions associated with images from elsewhere in the batch [29].
Revised: Negative samples (i.e., captions that do not match the observation) are captions associated with images from elsewhere in the batch [29].

Original: To perform this prediction,an auxiliary classifier D is attached to the network of the agent that encodes both visual and textobservations in a separate training pass from the behavioral cloning optimization.
Revised: To perform this prediction, an auxiliary classifier D is attached to the network of the agent that encodes both visual and text observations in a separate training pass from the behavioral cloning optimization.

Original: [Equation]
Revised: [Equation]

Original: Here o X represents the visual observation, and the caption sample is fed as a text observation.
Revised: Here o X represents the visual observation, and the caption sample is fed as a text observation.

Original: Theroll () function rolls the index over batches, so that y c roll ( b ) represents a caption from another batchelement as a negative example.
Revised: The roll () function rolls the index over batches, so that y c roll ( b ) represents a caption from another batch element as a negative example.

Original: The roll function was implemented as roll ( b ) =
Revised: The roll function was implemented as roll ( b ) =

Original: ( b + 1) mod B .
Revised: ( b + 1) mod B .

Original: And finally, the behavioral cloning loss over human language and movement action sequences is asdescribed in Interactive Agents Team [22]:
Revised: And finally, the behavioral cloning loss over human language and movement action sequences is as described in Interactive Agents Team [22]:

Original: [Equation]
Revised: [Equation]

Original: where the π m and a m represents the movement policy and action, respectively.
Revised: where the π m and a m represents the movement policy and action, respectively.

Original: The total loss is the sum of these losses L =
Revised: The total loss is the sum of these losses L =

Original: L C + L CM + L BC .
Revised: L C + L CM + L BC .

Original: Other than the addition of the captionloss and caption-matching loss, we follow Interactive Agents Team [22] for the agent architectureand training hyper-parameters.
Revised: Other than the addition of the caption loss and caption-matching loss, we follow Interactive Agents Team [22] for the agent architecture and training hyper-parameters.

Original:  Experiments 
Revised:  Experiments 3.1 Semi-supervised captioning

Original: 3.1 Semi-supervised captioning

Original: Hypothesis.
Revised: Hypothesis.

Original: We first sought to validate our method of semi-supervised learning.
Revised: We first sought to validate our method of semi-supervised learning.

Original: We hypothesizedthat with the additional unlabeled images, the semi-supervised model would be more data efficient(with respect to labelled data) than a supervised baseline.
Revised: We hypothesized that with the additional unlabeled images, the semi-supervised model would be more data efficient (with respect to labelled data) than a supervised baseline.

Original: Data.
Revised: Data.

Original: As mentioned, the domain in which we tested our methods is called the Playhouse, whichoriginated in Interactive Agents Team [22].
Revised: As mentioned, the domain in which we tested our methods is called the Playhouse, which originated in Interactive Agents Team [22].

Original: The authors compiled approximately three years worth ofinteraction data, comprising about 2 billion image frames.
Revised: The authors compiled approximately three years worth of interaction data, comprising about 2 billion image frames.

Original: The authors have confirmed no personallyidentifiable information or offensive content is contained in the dataset and gave consent for us toaccess it
Revised: The authors have confirmed no personally identifiable information or offensive content is contained in the dataset and gave consent for us to access it.

Original: As there are no associated captions with any frames, we treat each frame in this dataset as asingle image and used all the data as our “unpaired” dataset
Revised: As there are no associated captions with any frames, we treat each frame in this dataset as a single image, and used all the data as our “unpaired” dataset.

Original: For the “paired” dataset, we engaged with crowd raters to provide corresponding captions for 78Kuniformly sampled images from the unpaired dataset.
Revised: For the “paired” dataset, we engaged with crowd raters to provide corresponding captions for 78K uniformly sampled images from the unpaired dataset.

Original: Raters were instructed to describe what theysaw in the image, with particular reference to a randomly selected object indicated in a bounding box.
Revised: Raters were instructed to describe what they saw in the image, with particular reference to a randomly selected object indicated in a bounding box.

Original: Detailed instructions are included in Appendix A.2.
Revised: Detailed instructions are included in Appendix A.2.

Original: On average, each image contain 12 .
Revised: On average, each image contain 12 .

Original: 9 objects,and its corresponding caption describe 1 .
Revised: 9 objects, and its corresponding caption describe 1 .

Original: 2 of them.
Revised: 2 of them.

Original: Evaluation.
Revised: Evaluation.

Original: We measured the log probability of captions in the validation set, CIDEr score [30],object precision and “color-object pair precision”.
Revised: We measured the log probability of captions in the validation set, CIDEr score [30], object precision and “color-object pair precision”.

Original: As the images were generated from the Playhouseenvironment, we had access to the ground truth object identities and colors present in each image,allowing us to check if any object and its color mentioned in a caption were indeed present in theimage.
Revised: As the images were generated from the Playhouse environment, we had access to the ground truth object identities and colors present in each image, allowing us to check if any object and its color mentioned in a caption were indeed present in the image.

Original: We calculated object precision to be the proportion of correct objects among all mentionedin sampled captions, normalized by the same calculation from human captions.
Revised: We calculated object precision to be the proportion of correct objects among all mentioned in sampled captions, normalized by the same calculation from human captions.

Original: The color-objectpair precision is calculated in the same manner, except for any mentions of an object and its color.
Revised: The color-object pair precision is calculated in the same manner, except for any mentions of an object and its color.

Original: As there are usually many objects in an image and captions tend to mention just a few, the recallcounterpart of this metric (number of correct color-object pairs divided by those present in the image)is not very informative.
Revised: As there are usually many objects in an image and captions tend to mention just a few, the recall counterpart of this metric (number of correct color-object pairs divided by those present in the image) is not very informative.

Original: Captions were greedily sampled for the calculation of CIDEr score objectprecision and color-object pair precision.
Revised: Captions were greedily sampled for the calculation of CIDEr score object precision and color-object pair precision.

Original: Results.
Revised: Results.

Original: Figure 3 shows sample data used to train the our models, as well as samples generatedfrom the generative semi-supervised model.
Revised: Figure 3 shows sample data used to train the our models, as well as samples generated from the generative semi-supervised model.

Original: Models were able to both produce coherent and relevantcaptions given an image (e.g., "I can see a white bed where a green ball and a green duck are onit") and also generated realistic visual depictions of the Playhouse when conditioned on a languagedescription.
Revised: Models were able to both produce coherent and relevant captions given an image (e.g., "I can see a white bed where a green ball and a green duck are on it") and also generated realistic visual depictions of the Playhouse when conditioned on a language description.

Original: Both semi-supervised methods performed better than the supervised baseline across log prob, CIDErand color-object pair precision, including settings where we artificially constrained the size of thelabeled dataset (Figure 4).
Revised: Both semi-supervised methods performed better than the supervised baseline across log prob, CIDEr and color-object pair precision, including settings where we artificially constrained the size of the labeled dataset (Figure 4).

Original: Notably, when using the full amount of data our semi-supervised methodsexceeded human color-object pair precision.
Revised: Notably, when using the full amount of data our semi-supervised methods exceeded human color-object pair precision.

Original: To further put the performance gain in context, byleveraging the unpaired dataset of images the semi-supervised methods performed at a level equivalentto training a supervised model on 3 × more labeled data [31].
Revised: To further put the performance gain in context, by leveraging the unpaired dataset of images the semi-supervised methods performed at a level equivalent to training a supervised model on 3 × more labeled data [31].

Original: Interestingly, the semi-supervised methods perform similarly in the object precision metric, despitean advantage in color-object pair precision.
Revised: Interestingly, the semi-supervised methods perform similarly in the object precision metric, despite an advantage in color-object pair precision.

Original: This result suggest that the semi-supervised methods mayhave learned to bind color to objects from the unlabelled images, and this results in a performancegain compared to pure supervised methods.[Figure]
Revised: This result suggest that the semi-supervised methods may have learned to bind color to objects from the unlabelled images, and this results in a performance gain compared to pure supervised methods.

Original: 3.2 Learning captions of new objects
Revised: 3.2 Learning captions of new objects

Original: Hypothesis.
Revised: Hypothesis.

Original: Upon validating our methodology and amassing the relevant data, we were well-poisedto ask the following questions: do semi-supervised methods of intra-agent speech allow a model toquickly learn to speak about a new object with little supervision?
Revised: Upon validating our methodology and amassing the relevant data, we were well-poised to ask the following questions: do semi-supervised methods of intra-agent speech allow a model to quickly learn to speak about a new object with little supervision?

Revised: i can see a white bed where a green ball and a green duck are on it.

Revised: i can see a olive green color bus on the floor in front of blue color chair and near the violet color duck

Original: Data.
Revised: Caption samples Language conditioned image samples [Figure] data there is a pink color plane which is placed in front of the pink color rack there is a white potted plant in the living room on the ledge i can see a violet color table and white ball on it, on the left there is a green bed, teddy on floor.

Original:  To address the hypothesis, we first chose an object – a drum – and filtered our labeled captiondata to remove all instances where the drum is either depicted visually or spoken about verbally.
Revised: [Figure] [Figure] Data. To address the hypothesis, we first chose an object – a drum – and filtered our labeled caption data to remove all instances where the drum is either depicted visually or spoken about verbally.

Original: While not strictly necessary, from the unlabeled dataset we also removed all instances where theunderlying (and unobserved) instruction included mention of a drum, which ensured that all instances[Figure] of drum appearances were truly “passive”.
Revised: While not strictly necessary, from the unlabeled dataset we also removed all instances where the underlying (and unobserved) instruction included mention of a drum, which ensured that all instances of drum appearances were truly “passive”.

Original: For example, the unlabeled data may have includedtrajectories of experience wherein a players was tasked with removing a ball from a shelf, but theremight have also happened to be a drum in view in the room.
Revised: For example, the unlabeled data may have included trajectories of experience wherein a players was tasked with removing a ball from a shelf, but there might have also happened to be a drum in view in the room.

Original: Evaluation.
Revised: Evaluation.

Original: We evaluated the model using three different metrics.
Revised: We evaluated the model using three different metrics.

Original: We first created a validationdataset of all captions mentioning drum with their associated image, and evaluated the model’saverage log likelihood on this drum dataset (drum caption log likelihood).
Revised: We first created a validation dataset of all captions mentioning drum with their associated image, and evaluated the model’s average log likelihood on this drum dataset (drum caption log likelihood).

Original: The drum recall, whichis the probability that them model mentions a drum given that a drum is present, measures how themodel recognizes drums in the images.
Revised: The drum recall, which is the probability that them model mentions a drum given that a drum is present, measures how the model recognizes drums in the images.

Original: We also calculate its counterpart, the probability that a drumis present given a drum is mentioned, as the drum precision, to measure the accuracy of any drummentions from the model.
Revised: We also calculate its counterpart, the probability that a drum is present given a drum is mentioned, as the drum precision, to measure the accuracy of any drum mentions from the model.

Original: The perfect scores for drum recall and drum precision are 1 as a resultof titering the drum data: we have removed all the data where drum is in the image or caption, andadded back a small amount where drum is mentioned in the caption.
Revised: The perfect scores for drum recall and drum precision are 1 as a result of titering the drum data: we have removed all the data where drum is in the image or caption, and added back a small amount where drum is mentioned in the caption.

Original: This has a consequence that forany image with drum (although very few), its caption will always mention drum.
Revised: This has a consequence that for any image with drum (although very few), its caption will always mention drum.

Original: Results.
Revised: Results.

Original: We then performed the same experiment as in Section 3.1 at varying levels of re-introducedlabeled drum data, as seen in Figure 4.
Revised: We then performed the same experiment as in Section 3.1 at varying levels of re-introduced labeled drum data, as seen in Figure 4.

Original: As expected, the model’s ability to speak about drums scaleswith the amount of labeled drum data with which it is trained.
Revised: As expected, the model’s ability to speak about drums scales with the amount of labeled drum data with which it is trained.

Original: Notably, the model’s recall reachesapproximately 70% given only 585 labeled samples of drums.
Revised: Notably, the model’s recall reaches approximately 70% given only 585 labeled samples of drums.

Original: In contrast, the supervised method,where the new drum data is trained in concatenation with the non-drum data, reaches just over 20%.
Revised: In contrast, the supervised method, where the new drum data is trained in concatenation with the non-drum data, reaches just over 20%.

Original: The increase of drum recall in the semi-supervised method is also accompanied with an increase ofprecision, where the semi-supervised method reaches 80%, versus 40% of the supervised method.
Revised: The increase of drum recall in the semi-supervised method is also accompanied with an increase of precision, where the semi-supervised method reaches 80%, versus 40% of the supervised method.

Original: These results indicate that the semi-supervised methods are able to learn about new objects, implicitly ,by virtue of their language-conditioned reconstruction objective.
Revised: These results indicate that the semi-supervised methods are able to learn about new objects, implicitly , by virtue of their language-conditioned reconstruction objective.

Original: That is, the mere presence of drumsin the visual observations was sufficient, in conjunction with a small amount of labeled drum captions,to elicit linguistic understanding of drums as relevant objects in the Playhouse.
Revised: That is, the mere presence of drums in the visual observations was sufficient, in conjunction with a small amount of labeled drum captions, to elicit linguistic understanding of drums as relevant objects in the Playhouse.

Original: [Figure]
Revised: [Figure]

Original: 3.3 Learning 
Revised: 3.3 Learning to manipulate a new object

Original: to manipulate a new object

Original: Hypothesis.
Revised: Hypothesis.

Original: As a final test we tackled the final question: Can the ability to quickly learn tospeak about an object confer advantages for learning behaviors involving the new object (such asmanipulating the object, or answering questions about it)?
Revised: As a final test we tackled the final question: Can the ability to quickly learn to speak about an object confer advantages for learning behaviors involving the new object (such as manipulating the object, or answering questions about it)?

Original: Methods and data.
Revised: Methods and data.

Original: To answer this question we leveraged the semi-supervised models built in
Revised: To answer this question we leveraged the semi-supervised models built in

Original: Section 3.2 to provide auxiliary intra-agent speech objectives.
Revised: Section 3.2 to provide auxiliary intra-agent speech objectives.

Original: While we employed a two-stage process(learning to caption, and subsequently learning from those captions to shape behavior) for expedienceand ease of experimental setup, in principle the two learning procedures could occur in parallel ina unified agent.
Revised: While we employed a two-stage process (learning to caption, and subsequently learning from those captions to shape behavior) for expedience and ease of experimental setup, in principle the two learning procedures could occur in parallel in a unified agent.

Original: The agent then proceeded with behavioral cloning-based training as in Interactive
Revised: The agent then proceeded with behavioral cloning-based training as in Interactive

Original: Agents Team [22] on data that was filtered to not include any drum-based instructions (resulting in9 , 344 removed interactions).
Revised: Agents Team [22] on data that was filtered to not include any drum-based instructions (resulting in 9 , 344 removed interactions).

Original: While after filter the dataset do not contain any demonstration of druminteraction, in some episodes the drum object is visible (larger than a pixel) in the background in4.4% of the image frames.
Revised: While after filter the dataset do not contain any demonstration of drum interaction, in some episodes the drum object is visible (larger than a pixel) in the background in 4.4% of the image frames.

Original: Evaluation.
Revised: Evaluation.

Original: We evaluated agent performance on two tasks: in the “lift drum” task, the agent receivesthe instruction “lift a drum”, and is required to find the drum in a randomized Playhouse environmentand lift it.
Revised: We evaluated agent performance on two tasks: in the “lift drum” task, the agent receives the instruction “lift a drum”, and is required to find the drum in a randomized Playhouse environment and lift it.

Original: In the “ask color drum” task, a single drum is spawned in the environment.
Revised: In the “ask color drum” task, a single drum is spawned in the environment.

Original: The agentreceives the instruction “what is the color of the drum?”, and is then required to output language thatanswers the question (See Appendix A.4 for details).
Revised: The agent receives the instruction “what is the color of the drum?”, and is then required to output language that answers the question (See Appendix A.4 for details).

Original: At the end of each episode, the task environmentgives out a score of 0 or 1, indicating whether the task is completed successfully (note that thesescores are used for evaluation purposes only, and not for training).
Revised: At the end of each episode, the task environment gives out a score of 0 or 1, indicating whether the task is completed successfully (note that these scores are used for evaluation purposes only, and not for training).

Original: We collected human performanceon each of the tasks.
Revised: We collected human performance on each of the tasks.

Original: Our participants receive an average of 0 .
Revised: Our participants receive an average of 0 .

Original: 98 for the “lift a drum” task, and 0 .
Revised: 98 for the “lift a drum” task, and 0 .for

Original: 51for the “ask color drum” task.
Revised: the “ask color drum” task.

Original: The low score of “ask color drum” is primary due to the stringentreward function that require exact match of the canonical color name, and the fact that many colorsare similar to the canonical name (for example, the canonical “cyan” color is can often referred as“light blue” by human participants).
Revised: The low score of “ask color drum” is primary due to the stringent reward function that require exact match of the canonical color name, and the fact that many colors are similar to the canonical name (for example, the canonical “cyan” color is can often referred as “light blue” by human participants).

Original: We calculate the final score by dividing the agent score by theaverage human score, so that a final score of 0 represents no success, and a 1 represents human-levelperformance.
Revised: We calculate the final score by dividing the agent score by the average human score, so that a final score of 0 represents no success, and a 1 represents human-level performance.

Original: Results.
Revised: Results.

Original: As seen in Figure 6, a baseline agent trained without any drum-based instructions performspoorly on drum-based evaluation tasks, indicating that there is minimal transfer to be had whenlearning from the base interaction dataset.
Revised: As seen in Figure 6, a baseline agent trained without any drum-based instructions performs poorly on drum-based evaluation tasks, indicating that there is minimal transfer to be had when learning from the base interaction dataset.

Original: On the other hand, a model trained with drum-basedinstructions (that is, one that trains on the 9 , 344 episodes that we had filtered) displays an expectedupper bound for this agent of just over 75% on "lifting drums", and just under 80% for answeringquestions about a drum’s color, similar to previously reported results [22].
Revised: On the other hand, a model trained with drum-based instructions (that is, one that trains on the 9 , 344 episodes that we had filtered) displays an expected upper bound for this agent of just over 75% on "lifting drums", and just under 80% for answering questions about a drum’s color, similar to previously reported results [22].

Original: Agents trained with both the caption loss and caption-matching loss, but without any drum-basedinteraction data achieve approximately 70% of the performance attained by models trained directlyon drum-based interaction data.
Revised: Agents trained with both the caption loss and caption-matching loss, but without any drum-based interaction data achieve approximately 70% of the performance attained by models trained directly on drum-based interaction data.

Revised: To confirm the generalisability of our methods, we repeated our experiments on the teddy bear object, and observed similar result (Appendix A.5).

Original: This is a substantial increase over the baseline model, and directlyindicates that learning to speak about drums, which is afforded by training a model on as few as585 labeleddrum examples using semi-supervised methods, allows an agent to subsequently exhibitbehaviors that implicate the manipulation or mention of drums, without affecting performance on aknown object (Appendix A.5).
Revised: This is a substantial increase over the baseline model, and directly indicates that learning to speak about an object, which is afforded by training a model on as few as 585 labeled examples using semisupervised methods, allows an agent to subsequently exhibit behaviors that implicate the manipulation or mention of the object, without affecting performance on other known objects (Appendix A.5).

Original: We then tested whether we could reduce the number of labeled captions even further, and observedthat with as few as 150 labeled captions we observed similar performance on drum-color questionanswering tasks, and only a slight decrease in drum lifting task performance. 
Revised: We then tested whether we could reduce the number of labeled captions even further, and observed that with as few as 150 labeled captions we observed similar performance on drum-color question answering tasks, and only a slight decrease in drum lifting task performance. [Figure]

Revised: While we created two procedural tasks to quantitatively evaluate the agent, due to the diversity of the interaction data we used to train the agent we hypothesized that the agent can perform more tasks with the novel object.

Revised: Figure 7 summarizes a typical interactive session with our main agent (“+CM” from Figure 6) where the agent is asked to manipulate the novel object drum and answer questions about it.

Revised: The agent can recognize and manipulate the novel object under a variety of tasks.

Revised: This further confirms that our method is general to many background tasks that the agent is able to perform

Original:   Related Work
Revised: [Figure]  Related Work

Original: Components.
Revised: Components.

Original: The modeling in this study builds on components developed for generative modeling(VQVAE-2: [32]) and contrastive learning (CLIP: [20]).
Revised: The modeling in this study builds on components developed for generative modeling (VQVAE-2: [32]) and contrastive learning (CLIP: [20]).

Original: Task generalization in embodied agents.
Revised: Task generalization in embodied agents.

Original: Our work is in the tradition of grounded languageresearch in virtual environments [33–35].
Revised: Our work is in the tradition of grounded language research in virtual environments [33–35].

Original: Recently, there have been efforts to demonstrate zeroshot task acquisition using a variety of means including with the use of pretrained models forlanguage encoders [36] or visual-language models for zero-shot navigation [37].
Revised: Recently, there have been efforts to demonstrate zeroshot task acquisition using a variety of means including with the use of pretrained models for language encoders [36] or visual-language models for zero-shot navigation [37].

Original: The majority ofthese works have focused on generalization to novel phrasings of task instructions [38, 39] with somerecent studies exploring generalization to new tasks given background knowledge from previoustasks [22, 40].
Revised: The majority of these works have focused on generalization to novel phrasings of task instructions [38, 39] with some recent studies exploring generalization to new tasks given background knowledge from previous tasks [22, 40].

Original: Similar to phenomena of zero-shot generalization in supervised classification andobject detection [20, 21, 41], these studies have often relied on a large, diverse dataset providing[Figure] supervision – where held-out tasks represent a novel recombination of parts of the training dataset (e.g.,“compositional” generalization or systematicity [42]).
Revised: Similar to phenomena of zero-shot generalization in supervised classification and object detection [20, 21, 41], these studies have often relied on a large, diverse dataset providing supervision – where held-out tasks represent a novel recombination of parts of the training dataset (e.g., “compositional” generalization or systematicity [42]).

Original: Another line of research has focused on fewshot generalization [43–46] through meta-learning over a distribution of tasks – with generalizationto other tasks within the distribution’s support.
Revised: Another line of research has focused on fewshot generalization [43–46] through meta-learning over a distribution of tasks – with generalization to other tasks within the distribution’s support.

Original: Our work is complementary to both lines of work –and presents a means by which a different learning objective can embody “mental practice” enablinggeneralisation.
Revised: Our work is complementary to both lines of work – and presents a means by which a different learning objective can embody “mental practice” enabling generalisation.

Original: Semi-supervised learning with natural language.
Revised: Semi-supervised learning with natural language.

Original: The semi-supervised methods we used to train theintra-agent speech module are related to many previous methods that use language as a latent variable.
Revised: The semi-supervised methods we used to train the intra-agent speech module are related to many previous methods that use language as a latent variable.

Original: For example, Kingma et al.
Revised: For example, Kingma et al.

Original: [13] developed a semi-supervised, variational autoencoder (VAE) withdiscrete latent variables, and this work has also been extended to use language as the latent variable.
Revised: [13] developed a semi-supervised, variational autoencoder (VAE) with discrete latent variables, and this work has also been extended to use language as the latent variable.

Original: VAEs with language latent variables have also been studied in language summarization [47], and inthe context of multi-agent communication with natural language [48, 49].
Revised: VAEs with language latent variables have also been studied in language summarization [47], and in the context of multi-agent communication with natural language [48, 49].

Original: Our work may present avaluable contribution to this area, showcasing the utility of modern generative and contrastive modelsin supporting data-efficient learning.
Revised: Our work may present a valuable contribution to this area, showcasing the utility of modern generative and contrastive models in supporting data-efficient learning.

Original: Our work’s analysis of the scaling curve [31] properties ofsemi-supervised captioning [50–52] may provide additional analytical tools for the field.
Revised: Our work’s analysis of the scaling curve [31] properties of semi-supervised captioning [50–52] may provide additional analytical tools for the field.

Original: Language-conditioned image generation.
Revised: Language-conditioned image generation.

Original: The use of language to create sophisticated, controllablegenerative models has seen rapid development in recent years.
Revised: The use of language to create sophisticated, controllable generative models has seen rapid development in recent years.

Original: Recent works based on diffusionmodels [53–55] and autoregressive models have produced some of the most impressive results.
Revised: Recent works based on diffusion models [53–55] and autoregressive models have produced some of the most impressive results.

Original: Notable work includes the DALL · E family of models [56, 57] and GLIDE [58].
Revised: Notable work includes the DALL · E family of models [56, 57] and GLIDE [58].

Original: Other work in thearea has also employed VAEs [59] and GANs [60–64].
Revised: Other work in the area has also employed VAEs [59] and GANs [60–64].

Original:  Conclusions and Future Work
Revised:  Conclusions and Future Work

Original: In this work we built a model for intra-agent speech by leveraging semi-supervised learning techniques.
Revised: In this work we built a model for intra-agent speech by leveraging semi-supervised learning techniques.

Original: We have first shown that the semi-supervised methods outperforms the supervised model in captioningby leveraging unlabeled images.
Revised: We have first shown that the semi-supervised methods outperforms the supervised model in captioning by leveraging unlabeled images.

Original: We further demonstrated that with 150 captions of a novel object,such models can help embodied agents execute interactive tasks with the novel object without havingto learn from direct interactive experience (zero-shot), reaching 70% performance of a baseline agenttrained with 9 , 000 interaction episodes.
Revised: We further demonstrated that with 150 captions of a novel object, such models can help embodied agents execute interactive tasks with the novel object without having to learn from direct interactive experience (zero-shot), reaching 70% performance of a baseline agent trained with 9 , 000 interaction episodes.

Original: Although we developed the method in a virtual environment, it can be easily generalized to real worldapplications, applied with either good or bad intent.
Revised: Although we developed the method in a virtual environment, it can be easily generalized to real world applications, applied with either good or bad intent.

Original: Embodied language agents acting as robots couldbe instructed to perform a wide manner of tasks, including harmful ones.
Revised: Embodied language agents acting as robots could be instructed to perform a wide manner of tasks, including harmful ones.

Original: Thus, if this work is to beapplied in real settings, we advise extra caution to minimize unintended or harmful consequences.
Revised: Thus, if this work is to be applied in real settings, we advise extra caution to minimize unintended or harmful consequences.

Original: This work is a first step into leveraging ideas from the psychology of inner and private speech inhumans.
Revised: This work is a first step into leveraging ideas from the psychology of inner and private speech in humans.

Original: While our source of speech was captioning, humans have a richer repertoire of techniques for intra-personal dialogue that affect modalities beyond vision.
Revised: While our source of speech was captioning, humans have a richer repertoire of techniques for intra-personal dialogue that affect modalities beyond vision.

Original: Direct next steps are, therefore, toexplore a possible role of intra-agent speech in abstracting over time, and observing its effects onplanning and reasoning.
Revised: Direct next steps are, therefore, to explore a possible role of intra-agent speech in abstracting over time, and observing its effects on planning and reasoning.

Revised: Acknowledgments and Disclosure of Funding

Revised: The authors would like to thank Felix Hill for critical discussion, Nathaniel Wong for help with the development of scripted probe tasks, Chris Dyer and Daan Wierstra for reviewing and comments on the manuscript, and Alex Goldin and Guy Scully for organizational support.

Original: Checklist
Revised: Checklist

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Revised: For each question, change the default [TODO] to [Yes] , [No] , or [N/A] .

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Revised: You are strongly encouraged to include a justification to your answer , either by referencing the appropriate section of your paper or providing a brief inline description.

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Revised: For example:

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [Yes] See Section ?.•
Revised: [Yes] See Section ?.

Original:  Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [No] The code and the data areproprietary.
Revised: [No] The code and the data are proprietary.

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

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Original: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’scontributions and scope?
Revised: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?

Original: [Yes](b) Did you describe the limitations of your work?
Revised: [Yes] (b) Did you describe the limitations of your work?

Original: [Yes] See Section 5.(c)
Revised: [Yes] See Section 5.

Original:  Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] SeeSection 5.(d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?
Revised: [Yes] See Section 5. (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes]2.
Revised: [Yes] 2.

Original: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?
Revised: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?

Original: [Yes](b) Did you include complete proofs of all theoretical results?
Revised: [Yes] (b) Did you include complete proofs of all theoretical results?

Original: [Yes]3.
Revised: [Yes] 3.

Original: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?
Revised: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?

Original: [No] The code andthe data are proprietary.(b) Did you specify all the training details (e g., data splits, hyperparameters, how theywere chosen)?
Revised: [No] The code and the data are proprietary. (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?

Original: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)?
Revised: [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [Yes] See Figure 4, 5 and 6.
Revised: [Yes] See Figure 4, 5 and 6.

Original: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?
Revised: (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] See Section 3.1.
Revised: [Yes] See Section 3.1.

Original: We trained ourmodels using Tensor Processing Units (TPUv3), in all experiments the early stopping criteria is reached within 150 K optimization steps.
Revised: We trained our models using Tensor Processing Units (TPUv3), in all experiments the early stopping criteria is reached within 150 K optimization steps.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] We have cited Interactive Agents Team [22].
Revised: [Yes] We have cited Interactive Agents Team [22].

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [N/A]
Revised: [N/A]

Original: The assets are proprietary.
Revised: The assets are proprietary.

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [N/A] (d) Did you discuss whether and how consent was obtained from people whose data you’re using/curating?
Revised: [N/A] (d) Did you discuss whether and how consent was obtained from people whose data you’re using/curating?

Original: [Yes] Consent for using the data was obtained from Interactive Agents Team [22] under proprietary contract.
Revised: [Yes] Consent for using the data was obtained from Interactive Agents Team [22] under proprietary contract.

Original: See Section 3.1.
Revised: See Section 3.1.

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?
Revised: (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?

Original: [Yes] See Section 3.1.
Revised: [Yes] See Section 3.1.

Original: Interactive Agents Team [22] has confirmed no personal identifiable information or harmful content is contained in the dataset.
Revised: Interactive Agents Team [22] has confirmed no personal identifiable information or harmful content is contained in the dataset.

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if[Table] applicable?
Revised: (a) Did you include the full text of instructions given to participants and screenshots, if applicable?

Original: [Yes] See Appendix A.2.
Revised: [Yes] See Appendix A.2.

Original: [Table] (b) Did you describe any potential participant risks, with links to Institutional Review[Table] Board (IRB) approvals, if applicable?
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?

Original: [Yes] The full details of our study design were[Table] reviewed by our institution’s independent ethical review committee.
Revised: [Yes] The full details of our study design were reviewed by our institution’s independent ethical review committee.

Original: See Appendix A.2.
Revised: See Appendix A.2.

Original: [Table] (c) Did you include the estimated hourly wage paid to participants and the total amount[Table] spent on participant compensation?
Revised: (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?

Original: [No] The compensation rates were reviewed by[Table] our institution’s independent ethical review committee as part of the study design.
Revised: [No] The compensation rates were reviewed by our institution’s independent ethical review committee as part of the study design.

Original: SeeAppendix A.2.
Revised: See Appendix A.2.

Original: [Table]

Original: A AppendixA.1 Contrastive cross-entropy approximates generative log-likelihood
Revised: A Appendix A.1 Contrastive cross-entropy approximates generative log-likelihood

Original: Consider a batch of images { x b } Bb =1 and a caption y corresponding to one of the images x j .
Revised: Consider a batch of images { x b } Bb =1 and a caption y corresponding to one of the images x j .

Original: Thiscaption will come either from the paired dataset or as a sample from the image-conditional languageencoder q ω ( y | x j ) .
Revised: This caption will come either from the paired dataset or as a sample from the image-conditional language encoder q ω ( y | x j ) .

Original: The posterior distribution over the classification of the image index c is
Revised: The posterior distribution over the classification of the image index c is

Original: [Equation]
Revised: [Equation]

Original: Therefore, the multi-class cross-entropy over the correct index is
Revised: Therefore, the multi-class cross-entropy over the correct index is

Original: [Equation]
Revised: [Equation]

Original: We can manipulate the third term into the form of an expectation
Revised: We can manipulate the third term into the form of an expectation

Revised: [Equation]

Revised: [Equation]

Revised: b

Original: [Equation]
Revised: [Equation]

Original: and for large B, we can approximate 1 B (cid:80)
Revised: and for large B, we can approximate 1 B (cid:80)

Original: b p ( x b | y ) p ( x b ) ≈ E p ( x ) (cid:2) p ( x | y [Equation] p ( x ) (cid:3) =
Revised: b p ( x b | y ) p ( x b ) ≈ E p ( x ) (cid:2) p ( x | y ) p ( x ) (cid:3) =

Original: (cid:82) x p ( x | y )
Revised: (cid:82) x p ( x | y )

Original: = 1 .
Revised: = 1 .

Original: In this largebatch limit, the multi-class cross-entropy is proportional to the language-conditional image decoderlog-likelihood up to constant factors in y :
Revised: In this large batch limit, the multi-class cross-entropy is proportional to the language-conditional image decoder log-likelihood up to constant factors in y :

Original: [Equation]
Revised: [Equation]

Original: ) + constant [Equation] Therefore, if we have a multi-class classifier that discriminates if a batch element y is paired to abatch element x , we can substitute the generative log likelihood with the classifier’s loss:
Revised: Therefore, if we have a multi-class classifier that discriminates if a batch element y is paired to a batch element x , we can substitute the generative log likelihood with the classifier’s loss:

Original: [Equation]
Revised: [Equation]

Original: and gradients with respect to q ω ( y | x j ) are the same in expectation.
Revised: and gradients with respect to q ω ( y | x j ) are the same in expectation.

Original: [Figure]
Revised: [Figure]

Original: You will be shown an image of a room, where one of the objects is highlighted with a dotted red rectangle .
Revised: You will be shown an image of a room, where one of the objects is highlighted with a dotted red rectangle .

Original: Follow the script, think of a description of the object and its surroundings and type it in the text field.
Revised: Follow the script, think of a description of the object and its surroundings and type it in the text field.

Original: The description should also have enough details such that another player in the room can easily find and understand which object you are referring to.
Revised: The description should also have enough details such that another player in the room can easily find and understand which object you are referring to.

Original: During data collection, the caption interface displayed a single frame randomly selected from human-human interaction data as described by [22] at a resolution of 320 × 240 .
Revised: During data collection, the caption interface displayed a single frame randomly selected from human-human interaction data as described by [22] at a resolution of 320 × 240 .

Original: An object from the image was randomly selected and highlighted by a bounding box.
Revised: An object from the image was randomly selected and highlighted by a bounding box.

Original: The participants were prompted to “[d]escribe the object in the red dotted rectangle and its location relative to other objects or landmarks”.
Revised: The participants were prompted to “[d]escribe the object in the red dotted rectangle and its location relative to other objects or landmarks”.

Original: After the participants input thecaption inthe text-box and clicked “submit”, theis refreshed andtext-box is clearedfor the of next
Revised: After the participants input the in and clicked “submit” the and is cleared of next

Original: Numbered in interface
Revised: Numbered The prompt

Original: The prompt for for the participants to

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: A.4 Details about lift / ask color tasks
Revised: A.4 Details about lift / ask color tasks

Original: We modified the Playhouse environment [22] to create the lift drum and ask color drum tasks.
Revised: We modified the Playhouse environment [22] to create the lift drum and ask color drum tasks.

Original: Thesetasks were used for evaluation only, and the agent was never trained in these tasks.
Revised: These tasks were used for evaluation only, and the agent was never trained in these tasks.

Original: For these tasks,
Revised: For these tasks,

Original: We first initialize a randomized playhouse environment as described in [22], which represents arandomized multi-room environment.
Revised: We first initialize a randomized playhouse environment as described in [22], which represents a randomized multi-room environment.

Original: We then spawn a drum object in the room where the agentavatar is spawned.
Revised: We then spawn a drum object in the room where the agent avatar is spawned.

Original: The color of the drum object is randomly selected from the following list of10 colors: “red”, “yellow”, “blue”, “white”, “green”, “pink”, “purple”, “orange”, “aquamarine”,“magenta”.
Revised: The color of the drum object is randomly selected from the following list of 10 colors: “red”, “yellow”, “blue”, “white”, “green”, “pink”, “purple”, “orange”, “aquamarine”, “magenta”.

Original: Lift task.
Revised: Lift task.

Original: In the lift task, the instruction “ Lift the drum. ” appears after a random delay of upto 10 seconds.
Revised: In the lift task, the instruction “ Lift the drum. ” appears after a random delay of up to 10 seconds.

Original: A reward of 1 is given if the agent lifts any drum object, and the episode terminatesafter reward is emitted.
Revised: A reward of 1 is given if the agent lifts any drum object, and the episode terminates after reward is emitted.

Original: If the agent lifts any other object, or times out after 2 minutes, the episodeterminates with a reward of 0 .
Revised: If the agent lifts any other object, or times out after 2 minutes, the episode terminates with a reward of 0 .

Original: Ask about color task.
Revised: Ask about color task.

Original: In the ask about color task, the instruction “ What is the color of thedrum? ” appears after a random delay of up to 10 seconds.
Revised: In the ask about color task, the instruction “ What is the color of the drum? ” appears after a random delay of up to 10 seconds.

Original: If the agent emits language that matchesthe color of the drum, a reward of 1 is given, and the episode terminates.
Revised: If the agent emits language that matches the color of the drum, a reward of 1 is given, and the episode terminates.

Original: Otherwise if the agentoutputs any other text, or times out after 2 minutes with no language output, the episode terminateswith a reward of 0.
Revised: Otherwise if the agent outputs any other text, or times out after 2 minutes with no language output, the episode terminates with a reward of 0.

Original: For each agent, we averaged rewards collected from 1 , 000 episodes for each task.
Revised: For each agent, we averaged rewards collected from 1 , 000 episodes for each task.

Original: We also collectedhuman scores on these tasks, and used it to normalize the agent reward to a range of [0 , 1] .
Revised: We also collected human scores on these tasks, and used it to normalize the agent reward to a range of [0 , 1] .

Original: Thehuman normalized score is reported in the manuscript.
Revised: The human normalized score is reported in the manuscript.

Original: A.5 Results on control objects
Revised: A.5 Results on control objects.

Revised: - D r u m - D r u m +

Revised: C M - T e d d y - T e d d y + C M

Revised: F u ll d a t a

Revised: P r o c edu

Revised: r a l sc o r e

Original: [Table]
Revised: [Table]

Original: Lift Drum
Revised: drum Ask

Revised: [Table]

Original: Teddy bear
Revised: teddy Ask

Original: S585G +CM / G +D
Revised: [Table] ( hu m a n n o r m a li z e d ) - D r u m - D r u m + C M - T e d d y - T e d d y + C M F u ll d a t a - D r u m - D r u m

Original: +M +CB
Revised: + C M - T e d d y - T e d d y + C M

Original: S585G +CM / G +D
Revised: F u ll d a t a - D r u m - D r u m + C M - T e d d y - T e d d y + C M F u ll d a t a

Original: +M +CB
Revised: P r o c edu r a l sc o r e ( hu m a n n o r m a li z e d )