Artículos Relacionados con Reinforcement Learning with Verifiable Rewards

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Dwarkesh Patel: The Next Generation of AI May Be Built Through Actual Work

In his latest podcast, Dwarkesh Patel explores the next paradigm for AI training. While current progress in fields like coding and math relies on Reinforcement Learning with Verifiable Rewards (RLVR), which requires tasks that are both verifiable and highly scalable ("grindable"), Patel questions whether this is sufficient for complex real-world objectives like starting a business, winning a legal case, or managing an organization. These tasks provide verifiable outcomes but lack the resetable, parallelizable environments needed for efficient RLVR training. Patel argues the key limitation of current models is their inability to convert valuable in-context learning from real deployment into permanent weight updates—a process he terms "learning back to the weights." He proposes two potential solutions: On-Policy Self-Distillation (OPSD), where a model distills knowledge from long, task-specific sessions back into its base weights, and "dreaming," where an AI constructs simulated environments from real-world observations to practice and refine strategies. Ultimately, Patel envisions a future training paradigm where AI advances not just through pre-training on static datasets but through continual, post-deployment learning from real-world experience. This shift would enable AI to move beyond "grindable" tasks and develop robust, generalizable agent capabilities for complex, real-world challenges.

marsbit06/28 23:49

Dwarkesh Patel: The Next Generation of AI May Be Built Through Actual Work

marsbit06/28 23:49

Anthropic Has Taught Models to Understand Morality and Opened a New Path for Distillation

Anthropic's research "Teaching Claude Why" reveals a new, data-efficient method for AI alignment. Instead of relying on massive reinforcement learning with punishment (RLHF), which only teaches models to mimic safe answers without true ethical understanding, they used a small dataset (3 million tokens) of "difficult advice." This data consisted of detailed moral deliberations, reasoning, and debates, teaching the model the *why* behind decisions. The key was "deliberation-enhanced" Supervised Fine-Tuning (SFT). The model was trained on responses that included a "chain of thought" (CoT) process based on a constitutional framework. This framework included top-level principles, practical heuristics (like the "1000-user test"), and an 8-factor utility calculator (evaluating harm probability, reversibility, consent, etc.) for weighing complex trade-offs. This approach dropped model misalignment rates from 22% to 3% and showed strong generalization to unseen scenarios. The success challenges the old belief that "SFT memorizes, RL generalizes." It shows that SFT can generalize powerfully if the training data has two features: 1) high prompt diversity (many different scenario types) and 2) CoT supervision (showing the reasoning steps, not just the final answer). The model learns the underlying *thinking framework*, not just surface-level behaviors. This method points to a new paradigm for training AI in "non-RLVR" domains—areas like ethics, creative writing, or strategy where there's no single verifiable answer. The formula is: Domain Constitution + Heuristics + Multi-Factor Deliberation Framework + Diverse Deliberative CoT Data = Generalized capability. It represents a new form of "distillation," moving competition from pure compute towards who can best structure expert knowledge into high-quality reasoning datasets.

marsbit05/15 10:55

Anthropic Has Taught Models to Understand Morality and Opened a New Path for Distillation

marsbit05/15 10:55

6 Major AI Paradigm Shifts in 2025: From RLVR Training and Vibe Coding to Nano Banana

Summary: In 2025, six key paradigm shifts are redefining the AI landscape. RLVR (Reinforcement Learning with Verifiable Rewards) has become a core training method, enabling models to develop reasoning-like strategies through optimization on objective tasks like math and coding. This has shifted computational focus from pre-training to extended RL training. The concept of "ghost" vs. "animal" intelligence highlights the unique, jagged capability profile of LLMs, which excel in verifiable domains but remain brittle elsewhere, leading to widespread skepticism of benchmark performance. Cursor emerged as a new application-layer paradigm, demonstrating how vertical-specific tools can orchestrate multiple LLM calls into complex workflows. Claude Code redefined local AI by running powerful coding agents directly on user devices, integrating deeply with private data and environments. "Vibe Coding" lowered the barrier to programming, allowing both amateurs and professionals to build software through natural language description. Finally, Google's Nano banana signaled the next major computing paradigm by moving beyond text to a multi-modal, graphical user interface for LLMs, better aligning with human visual and spatial cognition.

marsbit12/22 09:29

6 Major AI Paradigm Shifts in 2025: From RLVR Training and Vibe Coding to Nano Banana

marsbit12/22 09:29

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