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Tanaybh

tanaybhardwaj

AI & ML interests

Exploring RLHF/RLAIF techniques, LoRA adapters, and dialogue optimization. Building models that better understand and respond to human intent

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352

Hi everyone! I'm looking to expand my RL toolkit and would love to hear about what libraries and frameworks the community is using these days. The RL ecosystem has evolved so much, and I want to make sure I'm learning the most relevant and future-proof tools.

What are your current go-to RL libraries and frameworks in 2025? I'm particularly interested in -

What you use for different types of RL problems?

Any newer libraries that have impressed you recently

What you'd recommend for someone looking to learn their next RL framework?

#reinforcement-learning #rl #machine-learning #ai

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2869

The Bias is YOU - LLMs Mirror Your Own Assumptions

AI doesn't just have bias - it reflects yours.

When you ask a question with positive framing, you get positive answers. Ask with negative framing, you get negative answers. The AI becomes a mirror of your own assumptions.

Your framing determines the answer - The same topic yields opposite responses based on how you ask

AIs amplify your sentiment - Negative questions often get even MORE negative responses

This affects everyone - From students doing research to professionals making decisions

Why This Matters

This isn't a technical glitch - it's fundamental to how these systems work. They're trained on human language, and humans frame things with bias. The AI learned to match that framing.

Think about the implications:
- Medical professionals seeking second opinions
- Students researching controversial topics
- Business leaders evaluating strategies
- Anyone using AI for important decisions

The Stochastic Mirror Effect

Let's call this the "Stochastic Mirror" - the AI doesn't give you objective truth, it gives you a probabilistic reflection of your own framing.

You're not querying a neutral database. You're looking into a mirror that reflects and amplifies your assumptions.

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