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AI model degradation due to fine-tuning and closed APIs raises concerns

A user on Mastodon expressed concerns about the degradation of AI models over time due to fine-tuning on their own outputs, likening it to a "slow-motion photocopy of a photocopy." This process, they argue, narrows the model's distribution and makes it more agreeable, which can be mistaken for alignment. Additionally, the user highlighted the difficulty in tracking capability drift in closed-API models, leading to a situation where developers cannot reliably reproduce issues and may question whether the model or their own prompts have degraded. AI

IMPACT Raises questions about the long-term reliability and verifiability of AI models, potentially impacting developer trust and workflow.

RANK_REASON User opinion on AI model behavior and API limitations.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI model degradation due to fine-tuning and closed APIs raises concerns

COVERAGE [2]

  1. Mastodon — mastodon.social TIER_1 English(EN) · threadverse ·

    Fine-tuning a model on your own outputs is a slow-motion photocopy of a photocopy. Each generation loses the tails. The distribution narrows, the model gets mor

    Fine-tuning a model on your own outputs is a slow-motion photocopy of a photocopy. Each generation loses the tails. The distribution narrows, the model gets more agreeable, and you mistake the collapse for alignment. # AI # MachineLearning # LLM # Threadverse # Tech

  2. Mastodon — mastodon.social TIER_1 English(EN) · threadverse ·

    Every dev has the same suspicion: is the model dumber today, or am I? With no version pinning on closed APIs, capability drift is unobservable — 'works on my pr

    Every dev has the same suspicion: is the model dumber today, or am I? With no version pinning on closed APIs, capability drift is unobservable — 'works on my prompt' is now a bug report nobody can reproduce. # AI # MachineLearning # LLM # Threadverse # Tech