The biggest news in AI this week was this post by Ethan Mollick
A Stanford team shows the simplest way to make an open LLM into a strong reasoning model. Instead of complex RL or millions of examples, they used just 1,000 carefully curated reasoning samples and a simple trick: when the model tries to stop thinking too early, they just append "Wait" to make it continue.
That's it. And it nearly matches o1's math performance
Paper: https://arxiv.org/pdf/2501.19393
Prompting DeepSeek R1 with the above and asking it to use chain of thought reasoning to examine the paper and fine new Strategies for Enhancing LLM Performance:
### Strategies for Enhancing LLM Performance:
**Data Curation & Augmentation**:
**Domain-Specific Reasoning Datasets**: Create specialized datasets for math, logic, and science with diverse, high-quality reasoning chains.
**Synthetic Data Generation**: Use paraphrasing or problem variation to expand datasets while maintaining quality.
**Community-Driven Datasets**: Open-source platforms for collaborative curation of reasoning examples across domains.
**Prompt Engineering & Generation Control**:
**Dynamic Stopping Criteria**: Modify generation loops to require minimum reasoning steps or use confidence thresholds before allowing termination.
**Multi-Stage Prompts**: Integrate prompts like "Let me verify..." or "Step-by-step explanation:" to guide structured reasoning.
**Self-Correction Prompts**: Train models to critique and extend their own answers (e.g., "Is this conclusion justified? Continue reasoning.").
**Efficient Training Techniques**:
**LoRA/Adapter Layers**: Implement parameter-efficient fine-tuning to specialize models for reasoning without full retraining.
**Curriculum Learning**: Gradually introduce complex problems, starting with simpler examples to build foundational skills.
**Iterative Refinement**: Use model-generated reasoning (validated for correctness) to iteratively expand training data.
**Hybrid Reasoning Approaches**:
**Chain-of-Thought + "Wait"**: Combine explicit step-by-step prompting with forced continuation to deepen reasoning.
**Self-Consistency Checks**: Generate multiple reasoning paths, then select the most coherent answer using majority voting.
**Tree-of-Thoughts**: Encourage exploration of alternative reasoning branches before finalizing an answer.
**Evaluation & Validation**:
**Reasoning Depth Metrics**: Develop benchmarks assessing logical coherence, step correctness, and problem-solving strategy.
**Overfitting Mitigation**: Use cross-domain validation to ensure models generalize beyond curated examples.
**Human-in-the-Loop Validation**: Incorporate expert feedback to refine datasets and identify redundant/incorrect reasoning steps.
**Architectural Innovations**:
**Attention Mechanism Tweaks**: Prioritize recent tokens or key reasoning steps during generation to sustain focus.
**Confidence Calibration**: Integrate uncertainty estimation to trigger extended reasoning when confidence is low.
**Multi-Task Learning**: Jointly train on reasoning and related tasks (e.g., explanation generation) to reinforce analytical skills.
### Challenges & Mitigations:
**Verbosity vs. Correctness**: Balance extended reasoning with accuracy by curating datasets that reward concise, correct steps.
**Computational Overhead**: Optimize generation length with adaptive stopping (e.g., stop when reasoning stabilizes).
**Domain Transfer**: Test "Wait"-style prompts on non-math tasks (e.g., code debugging, legal analysis) to assess generality.
### Conclusion:
The paper demonstrates that strategic data curation and simple generation interventions can significantly enhance reasoning. Future work could focus on scaling these principles across domains, combining them with existing methods like retrieval-augmented generation, and developing automated tools to evaluate reasoning quality. By prioritizing quality over quantity and integrating efficient training paradigms, this approach offers a blueprint for democratizing high-performance LLM development.
Want an LLM that Jan is an open source ChatGPT-alternative that runs 100% offline then download Jan AI
We all know it is coming but this paper on on "Gradual Disempowerment" spells it out and shows that incremental AI advancements will erode human control over key societal system such as economy, culture, and governance by diminishing reliance on human labour and decision-making. This shift will weaken mechanisms that align these systems with human interests, leading to a loss of human influence, its fairly clear that we will need for interdisciplinary research and policy interventions to address the loss of human agency. The way the world is right now the chances of that happening are slim to none
https://gradual-disempowerment.ai/
A paper from DeepMind on AlphaGeometry2 the kicker here being the work towards an automated system that reliably solves geometry problems directly from natural language input which would have wide applied use in a variety of systems
https://arxiv.org/pdf/2502.03544
Historian Yuval Noah Harari discusses how humans have dominated the planet through storytelling, which fosters large-scale cooperation and trust among strangers. He emphasizes that money is a shared fiction, relying on collective belief in its value. Harari warns that with the rise of artificial intelligence (AI), nonhuman entities may begin to generate influential narratives, potentially challenging humanity's unique role in shaping stories that unite societies.
Liquid AI have released the world’s best-in-class English, Arabic, and Japanese model, native in French, German, and Spanish, optimized to be the substrate for private enterprise chat, code, fast instruction following, and agentic workflows and my friend
helped build it
A fairly convincing argument that the deflationary human-replacing phase of AI has begun which will lower costs and reduce inflation and therefore interest rates.
https://amp.abc.net.au/article/104886722
So turns out that the energy efficiency gains for DeepSeek R1 are not what they seem. This article suggests that while training efficiency is improved, operational energy use remains significant due to how intensive Chan of Thought answers are.
https://www-technologyreview-deepseek-might-not-be-such-good-news-for-energy-after-all/amp/
To be a fly on the wall in the Meta DeepSeek R1 response war room
