Getting started with AI agents (part 1): Capturing processes, roles and connections venturebeat.com/ai/getting-sta… via VentureBeat

Getting started with AI agents (part 2): Autonomy, safeguards and pitfalls venturebeat.com/ai/getting-sta… via VentureBeat

.Babak Hodjat explains why #DeepSeek isn’t the Sputnik moment it’s hyped up to be, but instead an important accelerator for enterprise #AI adoption. Read the full article here on @Techzine ➡️ bit.ly/44Faih2

New article! Discovering effective policies for land-use planning with neuroevolution 👉 bit.ly/3ZoUBHf By Daniel Young, Olivier Francon, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, Babak Hodjat & Risto Miikkulainen Open Climate Fix Cognizant

Evolution Strategies at Scale LLM Fine-Tuning Beyond Reinforcement Learning

discuss with author: huggingface.co/papers/2509.24…

Reinforcement Learning (RL) has long been the dominant method for fine-tuning, powering many state-of-the-art LLMs. Methods like PPO and GRPO explore in action space. But can we instead explore directly in parameter space? YES we can. We propose a scalable framework for

As noted in DeepSeek-R1 and other studies, RL fine-tuning has several limitations, including challenges with long-horizon and outcome-only rewards, low sample efficiency, high-variance credit assignment, instability, and reward hacking. ES sidesteps these issues: it perturbs

On the symbolic-reasoning Countdown task, ES beats PPO/GRPO across Qwen-2.5 (0.5B–7B) & Llama-3 (1B–8B) with huge gains. Moreover, as shown in TinyZero by Jiayi Pan and DeepSeek-R1, RL fails on small models like Qwen-0.5B — yet ES succeeds! 🚀

Another key advantage of ES fine-tuning is its reliability. It runs stably across seeds, barely depends on hyperparameters, and avoids reward hacking — all while skipping gradients and actor-critic setups. In the figure, you can see ES finds a much better reward–KL balance than

To recap — ES can outperform RL for LLM fine-tuning. No gradients. No reward hacking. Just stability, efficiency, and scalability. ES shows low variance across seeds, minimal hyperparameter sensitivity, and strong reward–KL tradeoffs — all without actor-critic complexity.

Nice to see an exploration of the potential for ES (evolution strategies) in LLM fine tuning! Many potential advantages are discussed in this thread from Yulu Gan ✈️ NeurIPS'25 .

Evolution Strategies can be applied at scale to fine-tune LLMs, and outperforms PPO and GRPO in many model settings! Fantastic paper “Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning” by Yulu Gan ✈️ NeurIPS'25, Risto Miikkulainen and team. arxiv.org/abs/2509.24372

🧠 AGI: A Reality Check As AGI hype grows (and billionaires build bunkers), we need grounded voices. Cognizant’s Babak Hodjat reminds us: “LLMs don’t have meta-cognition… they don’t know what they know.” bbc.com/news/articles/…

LLMs hit a ceiling on long, complex reasoning. Tiny errors compound fast. Cognizant AI Lab just showed a new path: MAKER, a multi-agent system that solved a 1,000,000-step reasoning task with zero errors. tinyurl.com/2v47u665 #multiagentsystems #LLMs

MAKER cognizant.com/content/cogniz…

New work from Cognizant AI lab: Solving a Million-step LLM Task with Zero Errors. Existing LLMs struggle on long task horizons as persistent error rates compound, even when the LLMs know how to solve the task. Apple’s “Illusion of thinking” demonstrated that state of the art

Our subtask breakdown was to provide an llm agent with the current Towers of Hanoi state and the last move made. The agent would then use first-to-ahead-by-k voting along with abnormal response flagging to decide what move it wanted to do and provide the board state for the next

Second, as I mentioned earlier, right now this framework is limited to tasks where decomposition is provided. We are preliminarily testing generalized methods that preform both subtasks and task decomposition, and we are seeing promising results on boosting arithmetic abilities