Agentic reinforcement learning (RL) enables LLM agents to improve continuously from environment rewards, yet the resulting policies do not systematically accumulate reusable strategies that generalize across tasks. Modular skills can provide such reusable strategies, yet existing skill-augmented RL methods decouple skill creation from policy optimization, risking adopting skills that conflict with the evolving policy. Inspired by Anthropic's Skill Creator, we introduce RESKILL, an RL-in-the-loop skill creation framework that reconciles skill evolution with policy learning. RESKILL exploits the group-wise structure of GRPO to naturally embed three mechanisms with only marginal additional overhead: (1) an assertion-driven skill creator that diagnoses failures from past experience and proposes conditional, trigger-based skill revisions; (2) within-group rollout sampling that enables controlled comparison of skill versions, capturing which version best supports the policy's ongoing learning; and (3) Thompson Sampling with adaptive discounting to balance exploration and exploitation in skill version selection as the policy evolves. Across several domains, RESKILL consistently outperforms existing memory and skill-based RL methods, with the largest gains on unseen tasks. Analysis of the skill lifecycle shows skills being automatically created, tested, refined, and pruned as the policy improves, demonstrating reconciled skill-policy co-evolution.

Skill-based reinforcement learning (RL) has emerged as a promising strategy to leverage prior knowledge for accelerated robot learning. Skills are typically extracted from expert demonstrations and are embedded into a latent space from which they can be sampled as actions by a high-level RL agent. However, this skill space is expansive, and not all skills are relevant for a given robot state, making exploration difficult. Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space. We firstly propose accelerating exploration in the skill space using state-conditioned generative models to directly bias the high-level agent towards only sampling skills relevant to a given state based on prior experience. Next, we propose a low-level residual policy for fine-grained skill adaptation enabling downstream RL agents to adapt to unseen task variations. Finally, we validate our approach across four challenging manipulation tasks that differ from those used to build the skill space, demonstrating our ability to learn across task variations while significantly accelerating exploration, outperforming prior works. Code and videos are available on our project website: https://krishanrana.github.io/reskill.

This year, LinkedIn named data scientist specialists and artificial intelligence practitioners among the top jobs that enterprises sought to fill. The salary range for an artificial intelligence practitioner is $124,000 to $150,000. For a data scientist specialist, the salary range is $100,000 to $130,000. Compare this with the average U.S. salary for programmers, which is under $100,000. If you're working for a company, you want to make as much money as you can -- but you also want to enjoy what you're doing, and you want the self-confidence and assurance that you're providing value in your job and that you yourself are valued.

Technologies like artificial intelligence (AI), machine learning (ML), and automation in all of its forms can augment human workers and enable them to pivot to more valuable work, and perform their jobs with more efficiency, safety, and ease. Yet there's justifiable concerns emerging regarding the potential of these technologies to displace human workers. Ronald van Loon is working in partnership with Protiviti, and was able to examine their recent study, Future of Work Top Risks Survey brief, which was conducted as a joint effort with NC State University, and lend his point of view as an industry analyst about the evolving dynamic between technology and the future of work. How we work changed dramatically over the course of the past year, leading to new remote and hybrid work models, changing workforce and employment trends, and ubiquitous technology adoption to accelerate the necessary transformation to sustain operations. Protiviti's findings indicate that the future of work is shaping up to be one of the most disruptive and definitive business challenges of the next decade.

To consistently deliver this integrated experience in turn requires a set of foundational capabilities that include technology, data, talent, and insights on value being created - for the learner, the business and all key stakeholders. Working in sync, these capabilities help make the overall learning experience interactive, fun, immersive and personalized. This drives not just the adoption and usage of learner-centric products and services, but also expand their reach and efficacy, at viable economics. Chegg and Pearson are two examples of firms focused on such learner-centric experiences and capabilities.

Technologies like artificial intelligence (AI), machine learning (ML), and automation in all of its forms can augment human workers and enable them to pivot to more valuable work, and perform their jobs with more efficiency, safety, and ease. Yet there's justifiable concerns emerging regarding the potential of these technologies to displace human workers. Ronald van Loon is working in partnership with Protiviti, and was able to examine their recent study, Future of Work Top Risks Survey brief, which was conducted as a joint effort with NC State University, and lend his point of view as an industry analyst about the evolving dynamic between technology and the future of work. How we work changed dramatically over the course of the past year, leading to new remote and hybrid work models, changing workforce and employment trends, and ubiquitous technology adoption to accelerate the necessary transformation to sustain operations. Protiviti's findings indicate that the future of work is shaping up to be one of the most disruptive and definitive business challenges of the next decade.

RPA and AI undoubtedly create efficiencies, but with those efficiencies comes an added human responsibility: monitoring results and ejecting, if not preventing, biases. It is clear that 2020 will bring forth a tipping point in enterprise adoption of smart automation – artificially intelligent software bots that work with human workers to automate manual, repetitive tasks. Prior to the COVID-19 pandemic, more than half of U.S. businesses were already using this technology in daily operations. As businesses and governments continue to respond to the pandemic and the economic aftermath, automation will be even more pivotal as the pace of maturity accelerates, and the global economy reacts to it. According to the McKinsey Global Institute, automation and advances in artificial intelligence (AI) will lead as many as 375 million workers, or roughly 14 percent of the global workforce, to reskill themselves by 2030 – more applicable than before as industries look to speed their recovery.

As AI adoption brings out changes in the workplace, workers are challenged to obtain needed AI skills and business leaders are working to adapt. And as the COVID-19 pandemic has led to a shift to online learning, companies such as Udacity--who have been in that business for years--are in a good position to help. Business leaders may be caught between competing objectives of continuing to deliver strong financial performance while making investments in hiring, workforce training and new technologies that support growth, suggested the author of a recent piece in Harvard Business Review. A team at the MIT-IBM Watson AI Lab has been studying how work is being changed by AI. "By examining these findings, we can create a roadmap for leaders intent on adapting their workforce and reallocating capital, while also delivering profitability," stated author Martin Fleming, a VP and Chief Economist at IBM. He made three suggestions for reskilling the workforce to better prepare for AI.

As per a recent study, 54% of the world's workforce will need reskilling and upskilling by 2022. Emerging technologies: RPA, artificial intelligence, data analytics are reshaping how organisations do business, engage with customers, and manage their operations. Gartner predicts 70% of organisations will integrate AI to assist employee productivity by 2021. So, there is a pressing need for global businesses to focus on future skills and talent management. Data-driven culture: A McKinsey survey reveals data-driven organisations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times as likely to be profitable.

This theme is also shown in Udemy's top emerging tech skills of 2020, which included Gatsby.js