First draft in public 😱 😆🤓
What’s the best way for an agent to build a semantically sound and syntactically correct knowledge base?
Dog fooding my course material means the first step is to define the task environment.
/Checks notes
Task Environment: The description of Performance, Environment, Actuators, and Sensors (PEAS). This provides a complete specification of the problem domain.
So how can I implement this 🤔
First I need to think on the domain, something different to the examples (e.
Here’s a “standard” progression of training methodologies:
PRE-Training - This is where the model gains broad knowledge, forming the foundation necessary for reasoning. CPT (Continued Pre-training) - Makes the model knowledgeable about specific domains. SFT (Supervised Fine-Tuning) - Makes the model skilled at specific tasks by leveraging knowledge it already has. RL (Reinforcement Learning) - Using methods like GRPO, DPO to align model behavior. Reasoning traces play different roles at each stage:
Source: Off Policy “zero RL” in simple terms
Results demonstrate that LUFFY encourages the model to imitate high-quality reasoning traces while maintaining exploration of its own sampling space.
Authors introduce policy shaping via regularized importance sampling, which amplifies learning signals for low-probability yet crucial actions under “off-policy” guidance.
The aspect that is still not clear to me is how there is any exploration of the solution space.
Based on conventional zero-RL methods such as GRPO, LUFFY introduces off-policy reasoning traces (e.g., from DeepSeek-R1) and combines them with models' on-policy roll-outs before advantage computation.
… However, naively combining off-policy traces can lead to overly rapid convergence and entropy collapse, causing the model to latch onto superficial patterns rather than acquiring genuine reasoning capabilities.
…genuine reasoning capabilities… I am not certain if the implication is that Deepseek-R1 can reason or that it is a reminder that no model cam genuinely reason.
Zero-RL applies reinforcement learning RL to base LM directly, eliciting reasoning potentials using models' own rollouts. A fundamental limitation worth highlighting: it is inherently “on-policy”, constraining learning exclusively to the model’s self-generated outputs through iterative trials and feedback cycles. Despite showing promising results, zero-RL is bounded by the base LLM itself.
A key characteristic is that it means a LLM can be trained without Supervised Fine Tuning (SFT).
You are doing Imitation Learning (specifically Behavioral Cloning) because the goal and mechanism involve mimicking the expert’s token sequences.
You are doing Transfer Learning (specifically Knowledge Distillation) because you are transferring reasoning knowledge from a teacher model to a student model.
You are not doing Off-Policy Reinforcement Learning because the learning process is supervised likelihood maximization, not reward maximization using RL algorithms.
Although the data itself is “off-policy” (not generated by the model being trained), the learning paradigm is supervised imitation, not RL.
Support Vector Machines (SVM) are a mathematical approach for classifying data by finding optimal separating hyperplanes, applicable even in non-linear scenarios using kernel methods.
The document discusses various search algorithms used by Intelligent Agents for navigating mazes, detailing their types, characteristics, tradeoffs, and implementations.
This text introduces key concepts and algorithms related to intelligent agents in AI, focusing on search terms, uninformed and informed search strategies, and adversarial search techniques.
This post is inspired by a conversation with a fellow Data Science and AI student. It’s from the conversation, co-authored with Claude. Hope it’s useful!
Were to begin? When you’re starting your data science journey with Python, one of the first roadblocks you’ll encounter is package management. If you’ve tried conda and found it frustrating (as many do), you’re not alone. Let’s explore two modern tools that make Python package management simpler and more reliable: pipx and uv.
The text reflects on the misuse of technology and ethics in Silicon Valley, highlighting the importance of awareness and compassion amidst current challenges.
Great podcast where she talks about why America is broken.
Ideological sabotage (which surprised me, I’d thought the initial perpetrators would have done it for money) of the scientific method to protect the “right of freedom” has done exactly the opposite…
It really feels like some Americas are stuck fighting against no longer existence foes, either the British tyranny and taxation without representation or the war between capitalism and communism.
During the first phase of the project, the robots will be trained with approximately 45 atomic skills such as grasping, picking, placing and transporting
A single action may need to be repeated up to 600 times a day by a data collector for the robots to learn from
10 key scenarios, including industrial, domestic, and tourism services
It is expected that the collection of over 10 million real-machine data entries will be achieved within the year
The analysis concludes that while the EU AI Act does not obstruct startups, it presents both challenges and opportunities for innovation within a complex regulatory landscape.
I hope people can rally around and stop the Baffons soon. 💪🏼
#BeingHuman
BBC news article is very clear…
The Russian president has given the US leader just enough to claim that he made progress towards peace in Ukraine, without making it look like he was played by the Kremlin.
