The Benefits of Exercise for Employees: How to Improve Productivity and Reduce Stress

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We have all been there- tried the workout apps, tweaked our schedules accordingly, and even adjusted our workflow, but there seem to be a few gaps when it comes to improving our productivity.

Well, what if we told you that there’s a tool that can significantly improve productivity and reduce stress, and it’s completely free?

We all wish to be more productive, multitask effortlessly, finish our deliverables on time, and give enough time to our families at home. To achieve this, we push ourselves harder to reach that competitive edge. However, after a point, burnout settles in and digs at our productivity.

Could exercise be the missing piece of the productivity routine? Physical exercise has been linked with increased productivity and decreased stress levels across a range of medical literature. Regular exercise elevates our mood, and makes us healthier- and in the workplace, this translates to feeling more motivated. In the following paragraphs, we will outline the benefits of exercise for boosting employee productivity. We will also discuss some exercises which can help employees achieve their physical and professional goals.

What can employers do to encourage fitness?

However, before we move on, it is important to also address the fact that most employees do not get the time to chase after their fitness goals for numerous reasons. Work stress and tight deadlines are undoubtedly some of the most pressing of those. This is why many corporations across the globe have started the policy of introducing exercise in the workplace. This is not a novel idea, since a lot of companies have stressed the importance of physical and mental fitness for improved productivity. This can be one of the best ways to motivate employees to do better.

Sitting for long hours at work is linked with lower mental wellness and decreased work productivity. Many employers have also undertaken interventions such as standing desks or offering employees time to work out during office hours to improve their mental acuity and work performance.

Many other companies have partnered with gyms and fitness centres to provide employees with free memberships to encourage fitness. This is because employers understand that these costs will be nothing compared to the benefits they will receive from employees’ improved health and work performance. It has been seen that such measures have also resulted in better employee retention.

How does exercise improve work productivity?

Let us now look at the benefits associated with physical activity for employees.

Stress relief

The effects of stress on the human body can be far-reaching. Stressed-out employees can display physical, emotional, and cognitive symptoms of stress. Exercise helps employees handle their stress levels in a better way by reducing cortisol levels (the stress hormone released by the body) and releasing endorphins, which can help the mind relax.

One of the most common ways employees tackle workplace stress is by binge eating. This triggers a vicious cycle of more stress since these snacks contain high levels of sodium, which simply stresses the body even more. Instead, employees can be motivated to take a workout break, such as going for a short walk or simply doing some yoga, whenever they feel too stressed.

Healthy heart

Cardiovascular conditions such as hypertension, hyperlipidemia and diabetes are among the most common health issues faced by employees across the globe. Moreover, these conditions can be very taxing on employers since they have to cover health insurance for their employees suffering from these.

Regular exercise promotes a healthy heart, which helps employees deal with health conditions. Moreover, when employees are less occupied with personal concerns, they tend to perform better at work. As they become fitter, the burden of insurance on employers also lessens. Ultimately, in this way, encouraging employees to live a healthy life through regular physical activity can be advantageous to employers in more ways than just one.

Improved concentration

Exercise not only stimulates the muscles of the body, it also improves blood circulation to the brain. It keeps the glucose and oxygen levels high within the body. All of this makes the brain focus harder on the work at hand. Moreover, exercise has been shown to stimulate the growth of neural pathways in the brain, which increases concentration.

When employees are focused, it is easier to stick to deadlines and deliver excellent results.

Reduced fatigue

It might be ironic that physical exercise can be useful to combat fatigue when working out in itself can be a pretty tiring job. However, regular exercise can improve energy levels and reduce fatigue. This is because exercise makes it easier to get slow-wave sleep, which is essential for the body to recharge and rejuvenate itself. A lot of stressed-out individuals always complain about not getting enough sleep. Therefore, they are tired and drowsy throughout the day, which results in decreased productivity at work.

Regular physical activity will improve your quality of sleep, making you more productive and energetic at work, so that you can perform at peak conditions.

Increased energy levels

The body can get a much-needed boost from exercise-induced hormones, which can have an immediate effect on energy levels. Each time we push our bodies to go harder during exercise, we also increase our energy levels. As a result of increased energy levels, exercise also leads to an improved mood. An optimistic outlook will help employees produce better work.

Moreover, exercise in any form helps people become more disciplined. This improves their time management skills, which is an excellent quality when it comes to working under tight deadlines.

When colleagues meet outside of the professional work environment fosters healthy personal and professional relationships. Allowing employees to socialise outside the work environment can be a driving force behind employee engagement; moreover, it shows that the company wants employees to thrive beyond the workspace.

Therefore, as we can see, the benefits of exercise are not only physical. Studies have shown that exercise helps in improving memory, aids in concentration and encourages creative thinking and quicker learning. These attributes are directly related to employee productivity and stress management. Regular exercise, therefore, is one of the easiest ways to boost productivity and output.

The best exercises to boost productivity

Exercise is the best medicine. When it comes to increasing productivity, low-intensity workouts have more benefits than high-intensive exercises. This is important to keep in mind, because a very intensive training routine may just end up making us even more tired. However, certain high-intensity exercises such as the EMOM workout can be very efficient for working professionals due to their time-saving factor.

Certain exercises are meant to increase productivity among employees, and some of these can be done while sitting at the desk!

Walking

Probably the simplest form of exercising- walking outdoors in nature- is one of the best ways to work out to improve productivity. This could be at any point of time throughout the day. While walking outdoors is ideal, using an elliptical machine at home or a treadmill at a gym can also be a good way to get on your steps for the day. Walking is a form of low-impact exercise that works on the whole body, burns a lot of calories and can freshen the mind.

Walking can be encouraged among employees in several ways, such as introducing standing desks, going out on their lunch break instead of eating at the office; using the stairs instead of the elevator, taking a quick stroll during breaks to recharge the mind, and so on.

Yoga

Yoga not only strengthens the body, it also helps clear the mind and this is very useful when it comes to making important business decisions. Yoga promotes mindfulness, which can be a great way to manage stress at work. When employees are less stressed, they generally produce better results. Yoga helps employees relax in their work environment. Some forms of yoga can also be performed while sitting at the desk.

Low-intensity aerobic workout

Jogging, swimming and other low-intensity exercises target the body’s core. These can help in elevating overall fitness more healthily and naturally. Low-intensity workouts are geared towards increasing productivity and mental fitness.

Strength training

Strength training exercises are vital to improving physical and mental alertness. This is because an increase in the heart rate while performing these exercises also leads to the brain getting dibs on the oxygen and blood supply. This sharpens the sense of awareness and alertness, which can be very useful for honing decision-making skills at work. These exercises also help employees retain more energy, which is reflected in their enthusiastic and optimistic approach to working.

Some examples of strength training include climbing stairs, hill walking, lifting weights, squats, push-ups and sit-ups. EMOM training can also be customised into strength training modules and performed for better efficiency.

Seated leg raisers

This is one of the exercises that can help in strengthening the lower body as well as combating lower back pain. Many working individuals complain of back and shoulder pain, which can be a hindrance to their productivity levels. Certain exercises are tailored to counter these aches and pains so that employees can work while doing them. Seated leg raisers simply involve putting the legs together, and raising them at intervals for a specific number of reps.

Shoulder shrugs

An exercise tailored to counter back and neck pain, shoulder shrugs are easy to do while you work. Moreover, they are completely inconspicuous and will not take any additional time. They involve raising both shoulders towards the ears, holding them for a few seconds, and continuing this over a certain number of reps. This is associated with lowering back and neck pain, which can help in boosting productivity and preventing drowsiness.

When to exercise for maximum productivity?

Thirty minutes of moderate to vigorous physical activity per week is recommended for a healthy lifestyle. When exercising to improve productivity, consistency and progress is more important than how many calories are burnt. It is important to remember that the motive of exercise in this case is to improve mental alertness and manage stress, instead of building muscles. This is why it is important to choose an exercise that is appealing since it will not seem like just another chore.

The timing of exercise is also very important, particularly for working professionals who are always under tight schedules. Balancing work and personal commitments as well as a moderate physical activity regimen can be quite difficult. This is why it is not recommended to work out at the end of the day since this will just make a tired body even more tired. Morning and early afternoon are the best times to exercise, since they prevent daytime drowsiness, improve energy levels and can help concentrate better.

Building habits can take some time. However, isn’t it better to have a ten-minute consistent workout over a 60-minute regimen that is seldom done? The less the time commitment, the more likely people are to build habits. Here are a few tips to include exercise in a busy routine:

Start small

Make sure to not aim for very ambitious timings right at the beginning of the workout journey. Starting small and building from there is far more important.

Always have fun

As mentioned earlier, it is important to choose an activity that stimulates us, rather than choosing the hardest or most straining exercise. People are more likely to stick with something they like doing, instead of something they feel like they are being forced to do.

Make a routine

Building a habit involves staying on a consistent routine, every day of the week. Giving up even for a single day can ruin a winning streak. This is why it is important to stick to a set routine for a few months before it evolves into a habit.

In Summary

It is time to get out of the mindset that exercise is only meant for athletes and bodybuilders. Exercise has a clear and concise effect on productivity, as we have seen. Increased productivity can be noticed within a few weeks of implementing a fairly moderate exercise regimen. Employers around the world should encourage employees to be more physically active since this will have a direct impact on the company’s bottom line as well.

About the Author

Sean Lynam is a fitness enthusiast and personal trainer by profession, and a freelance writer by passion. Sean writes and shares his knowledge for a range of fitness publications and nutrition brands.

The post The Benefits of Exercise for Employees: How to Improve Productivity and Reduce Stress 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.

Conclusion

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 vertex-llm-tuning-preview@google.com with your Google Cloud Project number and a summary of your use case.

Acknowledgements

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.