Developing Resilience and Maintaining Mental Well-being in the Remote Work Era

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In recent years, the remote work revolution has transformed the way we work, allowing individuals to work from the comfort of their homes or any location of their choice. While remote work offers flexibility and numerous advantages, it also presents unique challenges to our mental well-being.

The lack of social interactions, blurred boundaries between work and personal life, and increased autonomy require us to develop resilience and prioritise our mental health. In this comprehensive article, we will explore effective strategies for developing resilience and maintaining mental well-being in the remote work era.

Understanding the challenges of remote work

Though it’s not always simple, remote working seems like the corporate transformation we need. Like anything else, working remotely has drawbacks and difficulties. It can be difficult to manage your time while working remotely.


Isolation and loneliness

Remote work can often lead to feelings of isolation and loneliness due to the lack of daily face-to-face interactions with colleagues. The absence of spontaneous conversations and physical presence can impact our mental well-being. It is the biggest expressed worry among remote employees, and its repercussions can have an impact beyond simply the individual. Increased stress and poor decision-making are two signs of isolation. These are alarming qualities for someone with important responsibilities, especially for an employer. Unfortunately, being isolated also makes it challenging for employers to recognize these symptoms.

To combat isolation:

  • Engage in virtual team-building activities to foster connections with colleagues.
  • Seek online communities or forums related to your profession or interests.
  • Schedule regular check-ins or virtual coffee breaks with colleagues or friends.


Blurring boundaries between work and personal life

In a remote work setup, the boundaries between work and personal life can become blurred, leading to longer working hours and difficulties in switching off from work mode. This can result in increased stress and a diminished sense of well-being.

Due to the natural worry that individuals are working too much and aren’t receiving the rest their minds require to function properly, some companies have made an effort to support the work-life balance of their employees by limiting access to work systems outside of office hours.

To establish clear boundaries:

  • Create a designated workspace separate from your living area, if possible.
  • Set specific working hours and communicate them to colleagues and family members.
  • Implement routines that signify the start and end of the workday, such as taking a walk or practising a short mindfulness exercise.


Increased autonomy and self-motivation

Remote work requires individuals to take greater ownership of their work, manage their time effectively, and stay motivated without direct supervision. This shift in responsibility can be overwhelming for some and lead to decreased productivity and motivation.

Organisations must understand how motivation may be enabled in a remote working situation in order to safeguard the productivity and well-being of their employees because levels of motivation are related to both productivity and well-being of individuals.

To enhance autonomy and self-motivation:

  • Use time-blocking techniques to structure your day and allocate specific time slots for different tasks.
  • Set realistic goals and break them down into manageable steps.
  • Find strategies that work best for you to stay focused and motivated, such as creating a to-do list or using productivity apps.


Variations in time zones

Those awful time zones have something to do with feeling or being out of the loop. It’s possible that you wake up right as your buddy is about to go to bed. This means that you can’t always count on your teammate to be there to respond to an urgent question or take care of any other pressing issue.

Although advantageous, managing a virtual team across different time zones can be very difficult. If this issue is not handled carefully, virtual teams may come to feel distant and alone. Being a part of a silent team that doesn’t interact through chat, emails, video conferences, or other channels might make us feel alone and prevent us from collaborating and working as a team efficiently.


To deal with time zone variations:

  • Consider having a four hour overlap to avoid collaboration delays
  • Develop flexible working hours to allow team members work when they are most productive
  • Comprehend the various remote work communication tools and how to balance their applications.


Cultivating resilience in the remote work environment

Building resilience has become more important for both individuals and companies in the ever-changing world of remote work. The challenges of working remotely are particular, including isolation, a blurring of work-life boundaries, and a greater reliance on digital contact. The ability to adapt, recover from failures, and sustain wellbeing in the face of adversity is a trait that people who can flourish in this environment must possess. Professionals can handle problems successfully and find chances for growth and success in their remote work journey by applying the following:


Enhance problem-solving skills

Developing effective problem-solving skills equips you with the ability to overcome obstacles and navigate challenges more effectively. This skill set is invaluable in maintaining resilience in the face of adversity.


We frequently approach issues with trepidation and pessimism. That makes it harder for us to think clearly and objectively about a problem. We can face problems with optimism when we have a positive viewpoint.  That increases the likelihood that we’ll actually find a workable answer. The mindset that challenges are opportunities for progress and that a good result is possible sets the stage for the final result.

To enhance problem-solving skills:

  • Break down complex problems into smaller, more manageable tasks. Diagraming process flows can help break down tasks, making them easier to achieve.
  • Explore alternative solutions and consider different perspectives.
  • Seek input from colleagues or mentors who can provide fresh insights and advice.


Develop a growth mindset

A growth mindset is the conviction that you can improve your skills with work, criticism, and education. It is a crucial component of resiliency since it aids in overcoming obstacles, seizing opportunities, and adapting to change. But how can one develop a growth mindset?

Adopting a growth mindset is crucial for building resilience in the face of challenges. Embrace difficulties as opportunities for growth and learning rather than as setbacks. This mindset shift allows you to approach obstacles with a positive and adaptive attitude.

To develop a growth mindset:

  • Emphasise the process rather than the outcome and focus on what you can learn from each experience.
  • Practice self-reflection and self-awareness to identify areas for growth and improvement.
  • Surround yourself with positive and supportive individuals who encourage your personal and professional development.


Build strong support networks

Nurturing connections with colleagues, mentors, and like-minded individuals is essential for maintaining resilience and well-being in the remote work era. These relationships provide support, guidance, and opportunities for collaboration and personal growth.

Regularly communicate—and not just about work. It’s essential that you regularly engage in one-on-one communication with your remote employees to keep them informed of expectations, deadlines, and other important information. Although they don’t want to be micromanaged, your employees do want frequent chances to talk about both work-related and unrelated matters. It is vital to find out how each team member prefers to interact when working remotely.

To build strong support networks:

  • Participate in online communities or professional networks related to your industry or interests.
  • Join virtual meetups, webinars, or conferences to connect with like-minded professionals.
  • Seek out virtual mentorship opportunities to learn from experienced individuals in your field.

Practice self-compassion

Self-compassion involves treating yourself with kindness, understanding, and acceptance during challenging times. It enables you to prioritise self-care and develop a resilient mindset. Self-compassion versus self-judgement, shared humanity versus isolation, and mindfulness versus over-identification are the three basic elements of self-compassion.

To practise self-compassion:

  • Be mindful of your inner dialogue and replace self-criticism with self-encouragement and self-acceptance.
  • Set realistic expectations for yourself and acknowledge that everyone faces setbacks and challenges.
  • Engage in self-care activities that promote relaxation, such as practising mindfulness, engaging in hobbies, or taking breaks to do something you enjoy.

Maintaining mental well-being in the remote work era

Maintaining mental health has become crucial to both personal and professional success in the age of remote work. The conventional distinctions between work and home life are eroding, so working remotely presents its own special set of issues. This is due to factors including feelings of isolation, increasing screen usage, and the ongoing battle to achieve work-life balance. Individuals can successfully navigate the remote work environment and uphold their utmost mental wellbeing by putting self-care, connection, and boundary-setting tactics into practice.


To ensure long-term wellbeing, remote employees should consider the following:

Establish a healthy routine

Creating a structured daily routine is vital for maintaining mental well-being in a remote work environment. A routine helps establish a sense of normalcy, supports work-life balance, and enhances productivity.

To establish a healthy routine:

  • Set consistent wake-up and bedtime routines to maintain a regular sleep schedule.
  • Schedule breaks throughout the day to rest, recharge, and engage in activities unrelated to work.
  • Incorporate physical exercise, mindfulness practices, and leisure activities into your routine.


Engage in physical activity

Regular physical activity is crucial for managing stress, improving mood, and enhancing overall well-being. In a remote work setup, where physical movement may be limited, finding ways to incorporate exercise into your daily routine is essential.

To engage in physical activity:

  • Explore various at-home workout options, such as yoga, HIIT workouts, or online fitness classes.
  • Take short breaks during the workday to stretch or go for a walk.
  • Make physical activity a priority by scheduling it into your daily routine.


Practice mindfulness and stress management techniques

Practising mindfulness and stress management techniques can help reduce stress, increase self-awareness, and improve focus and mental clarity.

To incorporate mindfulness and stress management techniques:

  • Dedicate a few minutes each day to deep breathing exercises, meditation, or guided mindfulness sessions.
  • Practice gratitude by reflecting on the positive aspects of your work and personal life.
  • Journaling can be a helpful tool for processing emotions and reducing stress.


Take regular digital detoxes

Constant exposure to digital devices and online communication can contribute to feelings of overwhelm and burnout. Taking regular breaks from screens is crucial for mental well-being and maintaining a healthy work-life balance.

To take regular digital detoxes:

  • Set boundaries around technology use by designating specific times or days for digital detox.
  • Engage in activities that do not involve screens, such as reading a book, going for a nature walk, or pursuing a hobby.
  • Create a screen-free environment during leisure time to encourage relaxation and better sleep quality.


Seek professional support when needed

Recognizing when additional support is necessary and reaching out to mental health professionals is essential for maintaining mental well-being. Remote work should not be a barrier to seeking professional help if needed.

To seek professional support:

  • Research and explore online counselling or therapy services that provide remote support.
  • Reach out to your employer’s human resources department to inquire about available resources or mental health support programs.
  • Prioritise your mental health by seeking help when experiencing persistent feelings of stress, anxiety, or depression.


The remote work era offers incredible opportunities for flexibility and work-life integration. However, it also presents unique challenges to our mental well-being. By understanding and addressing these challenges, cultivating resilience, and implementing strategies to maintain mental well-being, we can thrive in the remote work environment.


Despite the difficulties mentioned above, working remotely can be extremely rewarding—as long as you are aware of what to expect and are equipped to deal with these problems. If you stick with it, you’ll benefit from autonomy, flexibility, the opportunity to work in your ideal setting, increased productivity, and perhaps even more free time for a life outside of work.


Prioritise self-care, build strong support networks, and seek professional help when needed. Together, we can embrace the opportunities remote work presents while nurturing our mental health and well-being.


Author bio:

Tobi is a writer and editor with years of experience creating compelling content. She currently works as a marketing associate at Venngage infographic maker.

The post Developing Resilience and Maintaining Mental Well-being in the Remote Work Era appeared first on The 6Q Blog.

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1) input, (2) rationale, and (3) output. Given the prompt, an LLM is able to mimic the triplet demonstration to generate the rationale for any new input. For instance, in a commonsense question answering task, given the input question “Sammy wanted to go to where the people are. Where might he go? Answer Choices: (a) populated areas, (b) race track, (c) desert, (d) apartment, (e) roadblock”, distilling step-by-step provides the correct answer to the question, “(a) populated areas”, paired with the rationale that provides better connection from the question to the answer, “The answer must be a place with a lot of people. Of the above choices, only populated areas have a lot of people.” By providing CoT examples paired with rationales in the prompt, the in-context learning ability allows LLMs to output corresponding rationales for future unseen inputs.

We use the few-shot CoT prompting, which contains both an example rationale (highlighted in green) and a label (highlighted in blue), to elicit rationales from an LLM on new input examples. The example is from a commonsense question answering task.

After the rationales are extracted, in the second stage, we incorporate the rationales in training small models by framing the training process as a multi-task problem. Specifically, we train the small model with a novel rationale generation task in addition to the standard label prediction task. The rationale generation task enables the model to learn to generate the intermediate reasoning steps for the prediction, and guides the model to better predict the resultant label. We prepend task prefixes (i.e., [label] and [rationale] for label prediction and rationale generation, respectively) to the input examples for the model to differentiate the two tasks.

Experimental setup

In the experiments, we consider a 540B PaLM model as the LLM. For task-specific downstream models, we use T5 models. For CoT prompting, we use the original CoT prompts when available and curate our own examples for new datasets. We conduct the experiments on four benchmark datasets across three different NLP tasks: e-SNLI and ANLI for natural language inference; CQA for commonsense question answering; and SVAMP for arithmetic math word problems. We include two sets of baseline methods. For comparison to few-shot prompted LLMs, we compare to few-shot CoT prompting with a 540B PaLM model. In the paper, we also compare standard task-specific model training to both standard fine-tuning and standard distillation. In this blogpost, we will focus on the comparisons to standard fine-tuning for illustration purposes.

Less training data

Compared to standard fine-tuning, the distilling step-by-step method achieves better performance using much less training data. For instance, on the e-SNLI dataset, we achieve better performance than standard fine-tuning when using only 12.5% of the full dataset (shown in the upper left quadrant below). Similarly, we achieve a dataset size reduction of 75%, 25% and 20% on ANLI, CQA, and SVAMP.

Distilling step-by-step compared to standard fine-tuning using 220M T5 models on varying sizes of human-labeled datasets. On all datasets, distilling step-by-step is able to outperform standard fine-tuning, trained on the full dataset, by using much less training examples.

Smaller deployed model size

Compared to few-shot CoT prompted LLMs, distilling step-by-step achieves better performance using much smaller model sizes. For instance, on the e-SNLI dataset, we achieve better performance than 540B PaLM by using a 220M T5 model. On ANLI, we achieve better performance than 540B PaLM by using a 770M T5 model, which is over 700X smaller. Note that on ANLI, the same 770M T5 model struggles to match PaLM’s performance using standard fine-tuning.

We perform distilling step-by-step and standard fine-tuning on varying sizes of T5 models and compare their performance to LLM baselines, i.e., Few-shot CoT and PINTO Tuning. Distilling step-by-step is able to outperform LLM baselines by using much smaller models, e.g., over 700× smaller models on ANLI. Standard fine-tuning fails to match LLM’s performance using the same model size.

Distilling step-by-step outperforms few-shot LLMs with smaller models using less data

Finally, we explore the smallest model sizes and the least amount of data for distilling step-by-step to outperform PaLM’s few-shot performance. For instance, on ANLI, we surpass the performance of the 540B PaLM using a 770M T5 model. This smaller model only uses 80% of the full dataset. Meanwhile, we observe that standard fine-tuning cannot catch up with PaLM’s performance even using 100% of the full dataset. This suggests that distilling step-by-step simultaneously reduces the model size as well as the amount of data required to outperform LLMs.

We show the minimum size of T5 models and the least amount of human-labeled examples required for distilling step-by-step to outperform LLM’s few-shot CoT by a coarse-grained search. Distilling step-by-step is able to outperform few-shot CoT using not only much smaller models, but it also achieves so with much less training examples compared to standard fine-tuning.


We propose distilling step-by-step, a novel mechanism that extracts rationales from LLMs as informative supervision in training small, task-specific models. We show that distilling step-by-step reduces both the training dataset required to curate task-specific smaller models and the model size required to achieve, and even surpass, a few-shot prompted LLM’s performance. Overall, distilling step-by-step presents a resource-efficient paradigm that tackles the trade-off between model size and training data required.

Availability on Google Cloud Platform

Distilling step-by-step is available for private preview on Vertex AI. If you are interested in trying it out, please contact with your Google Cloud Project number and a summary of your use case.


This research was conducted by Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Thanks to Xiang Zhang and Sergey Ioffe for their valuable feedback.

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