Steering Gemini with BiDPO

Link post

Coauthored with Mark Kurzeja

A while back, we explored the “BiDPO” method for training steering vectors. In Gemini 1.5v1 Flash and Pro, BiDPO steering vectors boosted TruthfulQA scores by >10% while mostly retaining capabilities. When we updated to Gemini 1.5v2, prompt-based steering baselines became significantly stronger. BiDPO did not beat the stronger baselines, ending the project.

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BiDPO seems effective and sample-efficient but does not currently exceed more standard baselines. It’s hard to draw firm conclusions about BiDPO because TruthfulQA might not be measuring truthfulness /factuality. However, we remain excited about DPO-driven Conditional Activation Steering, which has additional advantages—particularly for targeted loss mitigation.

This result is largely negative. I wanted to share it to increase scientific understanding around steering! We also conducted a postmortem on why the method stopped outperforming baselines.

I’d also like to note that @ryan_greenblatt’s skepticism predicted this outcome more strongly than my worldview did. I want him to get points for that. :) While I think steering has targeted applications and provides clues about how LLMs function, it’s not a slam-dunk Pareto improvement on benchmarks we care about.

Read at https://​​turntrout.com/​​gemini-steering![1]

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