I work primarily on AI Alignment. Scroll down to my pinned Shortform for an idea of my current work and who I'd like to collaborate with.

Website: https://jacquesthibodeau.com

Twitter: https://twitter.com/JacquesThibs

GitHub: https://github.com/JayThibs 

LinkedIn: https://www.linkedin.com/in/jacques-thibodeau/ 


On Becoming a Great Alignment Researcher (Efficiently)

Wiki Contributions


I shared the following as a bio for EAG Bay Area 2024. I'm sharing this here if it reaches someone who wants to chat or collaborate.

Hey! I'm Jacques. I'm an independent technical alignment researcher with a background in physics and experience in government (social innovation, strategic foresight, mental health and energy regulation). Link to Swapcard profile. Twitter/X.


  • Collaborating with Quintin Pope on our Supervising AIs Improving AIs agenda (making automated AI science safe and controllable). The current project involves a new method allowing unsupervised model behaviour evaluations. Our agenda.
  • I'm a research lead in the AI Safety Camp for a project on stable reflectivity (testing models for metacognitive capabilities that impact future training/alignment).
  • Accelerating Alignment: augmenting alignment researchers using AI systems. A relevant talk I gave. Relevant survey post.
  • Other research that currently interests me: multi-polar AI worlds (and how that impacts post-deployment model behaviour), understanding-based interpretability, improving evals, designing safer training setups, interpretable architectures, and limits of current approaches (what would a new paradigm that addresses these limitations look like?).
  • Used to focus more on model editing, rethinking interpretability, causal scrubbing, etc.


  • How do you expect AGI/ASI to actually develop (so we can align our research accordingly)? Will scale plateau? I'd like to get feedback on some of my thoughts on this.
  • How can we connect the dots between different approaches? For example, connecting the dots between Influence Functions, Evaluations, Probes (detecting truthful direction), Function/Task Vectors, and Representation Engineering to see if they can work together to give us a better picture than the sum of their parts.
  • Debate over which agenda actually contributes to solving the core AI x-risk problems.
  • What if the pendulum swings in the other direction, and we never get the benefits of safe AGI? Is open source really as bad as people make it out to be?
  • How can we make something like the d/acc vision (by Vitalik Buterin) happen?
  • How can we design a system that leverages AI to speed up progress on alignment? What would you value the most?
  • What kinds of orgs are missing in the space?


  • Examples of projects I'd be interested in: extending either the Weak-to-Strong Generalization paper or the Sleeper Agents paper, understanding the impacts of synthetic data on LLM training, working on ELK-like research for LLMs, experiments on influence functions (studying the base model and its SFT, RLHF, iterative training counterparts; I heard that Anthropic is releasing code for this "soon") or studying the interpolation/extrapolation distinction in LLMs.
  • I’m also interested in talking to grantmakers for feedback on some projects I’d like to get funding for.
  • I'm slowly working on a guide for practical research productivity for alignment researchers to tackle low-hanging fruits that can quickly improve productivity in the field. I'd like feedback from people with solid track records and productivity coaches.


  • Strong math background, can understand Influence Functions enough to extend the work.
  • Strong machine learning engineering background. Can run ML experiments and fine-tuning runs with ease. Can effectively create data pipelines.
  • Strong application development background. I have various project ideas that could speed up alignment researchers; I'd be able to execute them much faster if I had someone to help me build my ideas fast. 

We're doing a hackathon with Apart Research on 26th. I created a list of problem statements for people to brainstorm off of.

Pro-active insight extraction from new research

Reading papers can take a long time and is often not worthwhile. As a result, researchers might read too many papers or almost none. However, there are still valuable nuggets in papers and posts. The issue is finding them. So, how might we design an AI research assistant that proactively looks at new papers (and old) and shares valuable information with researchers in a naturally consumable way? Part of this work involves presenting individual research with what they would personally find valuable and not overwhelm them with things they are less interested in.

How can we improve the LLM experience for researchers?

Many alignment researchers will use language models much less than they would like to because they don't know how to prompt the models, it takes time to create a valuable prompt, the model doesn't have enough context for their project, the model is not up-to-date on the latest techniques, etc. How might we make LLMs more useful for researchers by relieving them of those bottlenecks?

Simple experiments can be done quickly, but turning it into a full project can take a lot of time 

One key bottleneck for alignment research is transitioning from an initial 24-hour simple experiment in a notebook to a set of complete experiments tested with different models, datasets, interventions, etc. How can we help researchers move through that second research phase much faster?

How might we use AI agents to automate alignment research?

As AI agents become more capable, we can use them to automate parts of alignment research. The paper "A Multimodal Automated Interpretability Agent" serves as an initial attempt at this. How might we use AI agents to help either speed up alignment research or unlock paths that were previously inaccessible?

How can we nudge research toward better objectives (agendas or short experiments) for their research?

Even if we make researchers highly efficient, it means nothing if they are not working on the right things. Choosing the right objectives (projects and next steps) through time can be the difference between 0x to 1x to +100x. How can we ensure that researchers are working on the most valuable things?

What can be done to accelerate implementation and iteration speed?

Implementation and iteration speed on the most informative experiments matter greatly. How can we nudge them to gain the most bits of information in the shortest time? This involves helping them work on the right agendas/projects and helping them break down their projects in ways that help them make progress faster (and avoiding ending up tunnel-visioned on the wrong project for months/years). 

How can we connect all of the ideas in the field?

How can we integrate the open questions/projects in the field (with their critiques) in such a way that helps the researcher come up with well-grounded research directions faster? How can we aid them in choosing better directions and adjust throughout their research? This kind of work may eventually be a precursor to guiding AI agents to help us develop better ideas for alignment research.

Good to know! So I guess people were expecting that every company is running a “check if canary string is anywhere in our entire dataset and remove document if so” function?

If you just google the string, there are many instances of people sharing it verbatim. Would be good to do further testing to know if it was actually trained on the benchmark or learned through many other sources.

I send some related project ideas to @RogerDearnaley via DMs, but figured I should share them here to in case someone would like to give feedback or would like to collaborate on one of them.

I think data is underrated among the alignment community (synthetic/transformed data even more). I have been thinking about it from the perspective of pre-training and post-training. My initial look into synthetic data was related to online learning and essentially controlling model behaviour. I was interested in papers like this one by Google, where they significantly reduce sycophancy in an LLM via 1k synthetically generated examples. Data shapes behaviour, and I think many people do not acknowledge this enough (which sometimes leads them to make confused conclusions about model behaviour).

In terms of specific research projects, my current ideas fall into these kinds of buckets:

Pre-training close to the basin of attraction for alignment

  • How much can we improve "Pretraining Language Models with Human Preferences"? I'd like to transform training in various ways (as mentioned in your posts). For example, I could take fineweb and pre-train a GPT-2 sized model with the original dataset and a transformed version. Unclear so far which things I'd like to measure the most at that model size, though. A downstream experiment: is one model more likely to reward hack over the other? Does shard theory help us come up with useful experiments (pre-training with human feedback is almost like reinforcing behaviour and leveraging some form of shard theory)? Note that Google used a similar pre-training scheme for PaLM 2:
  • How can the "basin of attraction for alignment" be mathematically formalized?
  • Trying to the impact of systematic errors:
    • Studying reward misspecification: do the reward labels have a systematic effect and bias in pushing the model? How much of the model's behaviour is determined by the data itself vs. the reward model's misspecification? My current reading of the literature on this is a bit unclear. However, there's a paper saying: "We present a novel observation about the behaviour of offline reinforcement learning (RL) algorithms: on many benchmark datasets, offline RL can produce well-performing and safe policies even when trained with "wrong" reward labels, such as those that are zero everywhere or are negatives of the true rewards."
  • How do we design the training curriculum to significantly bias the model's pre-training close to the basin of attraction for alignment?
  • Studying some form of iterative training where we have a synthetically trained model vs a normally trained model and then measure things like model drift. For example, is the model more likely to drift (in an online setting) in ways we wouldn't want it to if it is pre-trained on normal text, but the process is more safely guided through synthetic pre-training?
  • Part of the alignment challenge (for example, the concern of scheming AIs) is that the order in which the model learns things might matter. For example, you'd want the model to internalize a solid world model of human values before it gains the situational awareness required to manipulate its training process (scheme). So, can we design a training curriculum for specific capabilities s.t. the model learns capabilities in an ideal sequence?

Data attribution project ideas

  • How to make this approach work in tandem with unlearning?
  • Use data attribution methods to understand how specific data shapes model behaviour and use that information to reconstruct pre-training to shape model behaviour in the way we want. For example, can we side-step the need for unlearning? Can these data attribution methods augment unlearning to work better?
    • As Roger said in his comment, we can try to manage the dataset to prevent WMB-dangerous capabilities and things like self-replication. It's quite possible that unlearning will not be enough.
    • Another project would be to fine-tune on a dataset with and without the dangerous capabilities we don't want and use that as a benchmark for unlearning methods (and how easy it is to fine-tune the capability back into the model).
  • Including other methods beyond data attribution (e.g. SAEs) to measure model evolution through training.
  • Is it possible to better understand and predict emergence via data attribution?
  • Studying model generalization via data attribution (doing similar things to the influence functions paper, but through time). Though the most interesting behaviour may only come at scales I wouldn't have the compute for.
  • Would there be value in using an early checkpoint in training and then training on the synthetic data from that point forward? At which point in training does this make sense to do?

It's cool that you point to @Tomek Korbak because I was wondering if we could think of ways to extend his Pretraining Language Models with Human Preferences paper in ways that Roger mentions in his post.

Happy to chat!

Just a heads up, it's been 2 months!

Recent paper I thought was cool:

In-Run Data Shapley: Data attribution method efficient enough for pre-training data attribution.

Essentially, it can track how individual data points (or clusters) impact model performance across pre-training. You just need to develop a set of validation examples to continually check the model's performance on those examples during pre-training. Amazingly, you can do this over the course of a single training run; no need to require multiple pre-training runs like other data attribution methods have required.

Other methods, like influence functions, are too computationally expensive to run during pre-training and can only be run post-training.

So, here's why this might be interesting from an alignment perspective:

  • You might be able to set up a bunch of validation examples to test specific behaviour in the models so that we are hyper-aware of which data points contribute the most to that behaviour. For example, self-awareness or self-preservation.
  • Given that this is possible to run during pre-training, you might understand model behaviour at such a granular level that you can construct data mixtures/curriculums that push the model towards internalizing 'human values' much sooner than it develops behaviours or capabilities we wouldn't want. Or, you delay self-awareness and such much further along in the training process.
  • In this @RogerDearnaley post, A "Bitter Lesson" Approach to Aligning AGI and ASI, Roger proposes training an AI on a synthetic dataset where all intelligences are motivated by the collective well-being of humanity. You are trying to bias the model to be as close to the basin of attraction for alignment as possible. In-Run Data Shapley could be used to construct such a dataset and guide the training process so that the training data best exemplifies the desired aligned behaviour.

If you are interested in this kind of research, let me know! I'd love to brainstorm some potential projects and then apply for funding if there is something promising there.

Ok, totally; there's no specific claim about ASI. Will edit the wording.

Hey @Zac Hatfield-Dodds, I noticed you are looking for citations; these are the interview bits I came across (and here at 47:31).

It's possible I misunderstood him; please correct me if I did!

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