wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The result implies the optimal sample complexity for offline learning and separates  local uniform OPE from the global case  . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Revised:  The result implies the optimal sample complexity for offline learning and separates local uniform OPE from the global case due to the extra S factor . Paramountly, the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings:
Edit intent: meaning-changed
Original: This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for finite horizon MDP) and provides a unified view towards optimal learning for several well-motivated offline tasks.
Revised: This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for episodic MDP) and provides a unified view towards optimal learning for several well-motivated offline tasks.
Edit intent: meaning-changed
Original: This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for finite horizon MDP) and provides a unified view towards optimal learning for several well-motivated offline tasks.
Revised: This work studies the statistical limits of uniform convergence for offline policy evaluation (OPE) problems with model-based methods (for finite horizon MDP) and provides a unified framework towards optimal learning for several well-motivated offline tasks.
Edit intent: clarity
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Edit intent: coherence
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Edit intent: meaning-changed
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline learning when \Pi contains all policies ( global policy class).
Edit intent: fluency
Original:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( global policy class).
Revised:  Uniform OPE \sup_\Pi|Q^\pi-Q^\pi|<\epsilon (initiated by \mbox{%DIFAUXCMD yin2021near is a stronger measure than the point-wise (fixed policy) OPE and ensures offline policy learning when \Pi contains all policies ( the global class).
Edit intent: fluency
Original:  In this paper, we establish an \Omega(H^2 S/d_m\epsilon^2) lower bound (over model-based family) for the global uniform OPE , where d_m is the minimal state-action probability induced by the behavior policy. Next, our main result establishes an episode complexity of O(H^2/d_m\epsilon^2) for  \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Revised:  In this paper, we establish an \Omega(H^2 S/d_m\epsilon^2) lower bound (over model-based family) for the global uniform OPE and our main result establishes an episode complexity of O(H^2/d_m\epsilon^2) for \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Edit intent: coherence
Original:  Next, our main result establishes an episode complexity of O(H^2/d_m\epsilon^2) for  \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Revised:  Next, our main result establishes an upper bound of O(H^2/d_m\epsilon^2) for \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Edit intent: clarity
Original:  Next, our main result establishes an episode complexity of O(H^2/d_m\epsilon^2) for  \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Revised:  Next, our main result establishes an episode complexity of O(H^2/d_m\epsilon^2) for the \emph{local} uniform convergence that applies to all \emph{near-empirically optimal} policies for the MDPs with \emph{stationary} transition.
Edit intent: fluency
Original:  This result implies the optimal sample complexity for offline learning and separates the local uniform OPE from the global case due to the extra S factor. Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Here d_m is the minimal marginal state-action probability. Critically, the highlight in achieving the optimal rate\tilde{O}\emph{ model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Edit intent: meaning-changed
Original:  Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Paramountly, the\tilde{O}(H^2/d_m\epsilon^2) is our design of\emph{ model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Edit intent: clarity
Original:  Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Paramountly, the\tilde{O}\emph{singleton absorbing MDP model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Edit intent: meaning-changed
Original:  Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Paramountly, the\tilde{O}\emph{ , which is a new sharp analysis tool that works with the model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Edit intent: meaning-changed
Original:  Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Paramountly, the\tilde{O}\emph{ model-based approach. We generalize such a model-based framework to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Edit intent: coherence
Original:  Paramountly, the\tilde{O}\emph{  model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively , which provides a unified framework for simultaneously solving different offline RL problems.
Revised:  Paramountly, the\tilde{O}\emph{ model-based method combining with our new analysis technique (singleton absorbing MDP) can be adapted to the new settings: offline task-agnostic and the offline reward-free with optimal complexity \tilde{O}(H^2\log(K)/d_m\epsilon^2) (K is the number of tasks) and \tilde{O}(H^2S/d_m\epsilon^2) respectively . These results provide a unified solution for simultaneously solving different offline RL problems.
Edit intent: coherence
Original:  By  modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-supervised manner, by automatically moving the end-effector's camera around the object.
Revised:  By then modelling a manipulation task as a coarse, approach trajectory followed by a fine, interaction trajectory, this state estimator can be trained in a self-supervised manner, by automatically moving the end-effector's camera around the object.
Edit intent: coherence
Original:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the  demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory  without actually needing to explicitly learn a policy.
Revised:  At test time, the end-effector moves to the estimated state through a linear path, at which point the demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory without actually needing to explicitly learn a policy.
Edit intent: fluency
Original:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the  demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory  without actually needing to explicitly learn a policy.
Revised:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the original demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory without actually needing to explicitly learn a policy.
Edit intent: clarity
Original:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the  demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory  without actually needing to explicitly learn a policy.
Revised:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the demonstration's end-effector velocities are simply replayed. This enables convenient acquisition of a complex interaction trajectory without actually needing to explicitly learn a policy.
Edit intent: clarity
Original:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the  demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory  without actually needing to explicitly learn a policy.
Revised:  At test time, the end-effector is moved to the estimated state through a linear path, at which point the demonstration's end-effector velocities are simply repeated, enabling convenient acquisition of a complex interaction trajectory , without actually needing to explicitly learn a policy.
Edit intent: fluency
Original:  We find that , even with relatively high levels of vaccine coverage , reopening may lead to infection prevalence well above current ambient community levels. As many students attending urban universities belong to vaccine hesitant groups, asymptomatic cases present increased collateral risk to these students' households and communities. We recommend that such universities ensure that most of the campus population is vaccinated, perform asymptomatic screening testing, limit the number of individuals on-campus, and encourage students to socialize safely.
Revised:  We find that mRNA vaccine coverage above 80\% makes it possible to safely reopen to in-person instruction. If vaccine coverage is 100\%, then our model indicates that facemask use is not necessary. Our simulations with vaccine coverage below 70\% exhibit a right-skew for total infections over the semester, which suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Less effective vaccines or incidence of new variants may require additional intervention such as screening testing to reopen safely.
Edit intent: meaning-changed
Original: Multiple studies have shown that BERT is remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
Revised: Multiple studies have shown that Transformers are remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
Edit intent: style
Original: Multiple studies have shown that BERT is remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
Revised: Multiple studies have shown that BERT is remarkably robust to pruning . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
Edit intent: clarity
Original: Multiple studies have shown that BERT is remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of scaling factors and biases in the output layer normalization (<0.0001\% of model weights).
Revised: Multiple studies have shown that BERT is remarkably robust to pruning , yet few if any of its components retain high importance across downstream tasks . Contrary to this received wisdom, we demonstrate that pre-trained Transformer encoders are surprisingly fragile to the removal of a very small number of features in the layer outputs (<0.0001\% of model weights).
Edit intent: clarity
Original:  These are high-magnitude normalization parameters that emerge early in pre-training and show up consistently in the same dimensional position throughout the model.
Revised:  In case of BERT and other pre-trained encoder Transformers, the affected component is the scaling factors and biases in the LayerNorm. The outliers are high-magnitude normalization parameters that emerge early in pre-training and show up consistently in the same dimensional position throughout the model.
Edit intent: meaning-changed
Original: The uniform information density (UID) hypothesis, which posits that speakers prefer utterances that distribute information uniformly across the signal, has gained substantial traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Revised: The uniform information density (UID) hypothesis, which posits that speakers behaving optimally tend to distribute information uniformly across the signal, has gained substantial traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Edit intent: meaning-changed
Original: The uniform information density (UID) hypothesis, which posits that speakers prefer utterances that distribute information uniformly across the signal, has gained substantial traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Revised: The uniform information density (UID) hypothesis, which posits that speakers prefer utterances that distribute information uniformly across a linguistic signal, has gained substantial traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Edit intent: clarity
Original: The uniform information density (UID) hypothesis, which posits that speakers prefer utterances that distribute information uniformly across the signal, has gained substantial traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Revised: The uniform information density (UID) hypothesis, which posits that speakers prefer utterances that distribute information uniformly across the signal, has gained traction in psycholinguistics as an explanation for certain syntactic, morphological, and prosodic choices.
Edit intent: clarity
Original:  To better utilize these samples, prioritized sampling is a promising technique to improve the performance of RL agents. Previous prioritization methods based on temporal-difference (TD ) errorare highly heuristic and divergent from the objective of RL . In this work, we analyze the optimal prioritization strategy that can minimize the regret of RL policytheoretically.
Revised:  To better utilize these samples, prioritized sampling is a promising technique to better utilize these samples. Previous criteria of prioritization include TD error, recentness and corrective feedback, which are mostly heuristically designed . In this work, we analyze the optimal prioritization strategy that can minimize the regret of RL policytheoretically.
Edit intent: meaning-changed
Original:  Previous prioritization methods based on temporal-difference (TD ) errorare highly heuristic and divergent from the objective of RL . In this work, we analyze the optimal prioritization strategy that can minimize the regret of RL policytheoretically.
Revised:  Previous prioritization methods based on temporal-difference (TD ) errorare highly heuristic and divergent from the objective of RL . In this work, we start from the regret minimization objective, and obtain an optimal prioritization strategy that can minimize the regret of RL policytheoretically.
Edit intent: meaning-changed
Original:  Previous prioritization methods based on temporal-difference (TD ) errorare highly heuristic and divergent from the objective of RL . In this work, we analyze the optimal prioritization strategy that can minimize the regret of RL policytheoretically. Our theory suggests that the data with higher  TD error, better on-policiness and more corrective feedback should be assigned with higher weights during sampling.
Revised:  Previous prioritization methods based on temporal-difference (TD ) errorare highly heuristic and divergent from the objective of RL . In this work, we analyze the optimal prioritization strategy for Bellman update that can directly maximize the return of the policy. The theory suggests that the data with higher TD error, better on-policiness and more corrective feedback should be assigned with higher weights during sampling.
Edit intent: meaning-changed
Original:  Based on this theory, we propose two practical algorithms, RM-DisCor and RM-TCE. RM-DisCor is a general algorithm and RM-TCE is a more efficient variant relying on the temporal ordering of states.
Revised:  Thus most previous criteria only consider this strategy partially. We not only provide theoretical justifications for previous criteria, but also propose two new methods to compute the prioritization weight, namely ReMERN and ReMERT. ReMERN learns an error network, while ReMERT exploits the temporal ordering of states.
Edit intent: meaning-changed
Original:  We further explore the effect of script vs. morphological typology in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Revised:  We further explore the effect of script vs. language similarity in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) morphologically distinct, but use the same script, b) morphologically similar, but use a distinct script, or c) are morphologically similar and use the same script.
Edit intent: clarity