You might enjoy this new blogpost from HuggingFace, which goes into more detail.

Phi-4: Synthetic data works. Pretraining's days are numbered. 

Microsoft just announced Phi-4,  a 14B parameter model that matches GPT-4o on some difficult benchmarks. The accompanying technical report offers a glimpse into the growing importance of synthetic data and how frontier model training is changing. 

Some takeaways:

• The data wall is looking flimsier by the day. Phi-4 is highly capable not despite but because of synthetic data. It was trained on a curriculum of 50 types of synthetic datasets, generated by GPT-4o from a diverse set of organic data "seeds". We're seeing a smooth progression from training on (1) organic data, to (2) human-curated datasets, to (3) AI-curated datasets (filtering for appropriate difficulty, using verifiers), to (4) AI-augmented data (generating Q&A pairs, iteratively refining answers, reverse-engineering instructions from code, etc.), to (5) pure synthetic data.
• Training is fracturing. It's not just the quality and mixture but also the ordering of data that matters. Phi-4 features a "midtraining" phase that expands its context length from 4k to 16k tokens, upweighting long-context behavior only when the model has become capable enough to integrate that extra information. Post-training features a standard SFT phase and two rounds of DPO: one round of DPO using "pivotal token search" to generate minimally distinct pairs that are easier to learn from, and one round of more standard "judge-guided DPO". In the author's own words: "An end-to-end optimization of pretraining data mixture that also takes into account the effects of post-training is an interesting future area of investigation."
• The next frontier is self-improvement. Phi-4 was taught by GPT-4; GPT-5 is being taught by o1; GPT-6 will teach itself. This progression towards online learning is possible because of amortization: additional inference-time compute spent generating higher quality tokens becomes training data. The techniques range from simple (rejection-sampling multiple answers and iterative refinement) to complex (o1-style reasoning), but the principle remains: AI systems will increasingly be involved in training their successors and then themselves by curating, enhancing, and generating data, and soon by optimizing their own training curricula. 

The implication: If you don't have access to a 2024-frontier AI, you're going to have a hard time training the next frontier model. That gap will likely widen with each subsequent iteration. 

The RL setup itself is straightforward, right? An MDP where S is the space of strings, A is the set of strings < n tokens, P(s'|s,a)=append(s,a) and reward is given to states with a stop token based on some ground truth verifier like unit tests or formal verification.

I agree that this is the most straightforward interpretation, but OpenAI have made no commitment to sticking to honest and straightforward interpretations. So I don't think the RL setup is actually that straightforward. 

If you want more technical detail, I recommend watching the Rush & Ritter talk (see also slides and bibliography). This post was meant as a high-level overview of the different compatible interpretations with some pointers to further reading/watching. 

The examples they provide one of the announcement blog posts (under the "Chain of Thought" section) suggest this is more than just marketing hype (even if these examples are cherry-picked):

Here are some excerpts from two of the eight examples:

Cipher:

Hmm.

But actually in the problem it says the example:

...

Option 2: Try mapping as per an assigned code: perhaps columns of letters?

Alternatively, perhaps the cipher is more complex.

Alternatively, notice that "oyfjdnisdr" has 10 letters and "Think" has 5 letters.

...

Alternatively, perhaps subtract: 25 -15 = 10.

No.

Alternatively, perhaps combine the numbers in some way.

Alternatively, think about their positions in the alphabet.

Alternatively, perhaps the letters are encrypted via a code.

Alternatively, perhaps if we overlay the word 'Think' over the cipher pairs 'oy', 'fj', etc., the cipher is formed by substituting each plaintext letter with two letters.

Alternatively, perhaps consider the 'original' letters.

Science: 

Wait, perhaps more accurate to find Kb for F^− and compare it to Ka for NH4+.
...
But maybe not necessary.
...
Wait, but in our case, the weak acid and weak base have the same concentration, because NH4F dissociates into equal amounts of NH4^+ and F^-
...
Wait, the correct formula is:

It's worth noting that there are also hybrid approaches, for example, where you use automated verifiers (or a combination of automated verifiers and supervised labels) to train a process reward model that you then train your reasoning model against. 

See also this related shortform in which I speculate about the relationship between o1 and AIXI: 

Agency = Prediction + Decision.

AIXI is an idealized model of a superintelligent agent that combines "perfect" prediction (Solomonoff Induction) with "perfect" decision-making (sequential decision theory).

OpenAI's o1 is a real-world "reasoning model" that combines a superhuman predictor (an LLM like GPT-4) with advanced decision-making (implicit search via chain of thought trained by RL).

[Continued]

Agency = Prediction + Decision.

AIXI is an idealized model of a superintelligent agent that combines "perfect" prediction (Solomonoff Induction) with "perfect" decision-making (sequential decision theory).

OpenAI's o1 is a real-world "reasoning model" that combines a superhuman predictor (an LLM like GPT-4) with advanced decision-making (implicit search via chain of thought trained by RL).

To be clear: o1 is no AIXI. But AIXI, as an ideal, can teach us something about the future of o1-like systems.

AIXI teaches us that agency is simple. It involves just two raw ingredients: prediction and decision-making. And we know how to produce these ingredients. Good predictions come from self-supervised learning, an art we have begun to master over the last decade of scaling pretraining. Good decisions come from search, which has evolved from the explicit search algorithms that powered DeepBlue and AlphaGo to the implicit methods that drive AlphaZero and now o1.

So let's call "reasoning models" like o1 what they really are: the first true AI agents. It's not tool-use that makes an agent; it's how that agent reasons. Bandwidth comes second.

Simple does not mean cheap: pretraining is an industrial process that costs (hundreds of) billions of dollars. Simple also does not mean easy: decision-making is especially difficult to get right since amortizing search (=training a model to perform implicit search) requires RL, which is notoriously tricky.

Simple does mean scalable. The original scaling laws taught us how to exchange compute for better predictions. The new test-time scaling laws teach us how to exchange compute for better decisions. AIXI may still be a ways off, but we can see at least one open path that leads closer to that ideal.

The bitter lesson is that "general methods that leverage computation [such as search and learning] are ultimately the most effective, and by a large margin." The lesson from AIXI is that maybe these are all you need. The lesson from o1 is that maybe all that's left is just a bit more compute...

We still don't know the exact details of how o1 works. If you're interested in reading about hypotheses for what might be going on and further discussion of the implications for scaling and recursive self-improvement, see my recent post, "o1: A Technical Primer"

Right. SLT tells us how to operationalize and measure (via the LLC) basin volume in general for DL. It tells us about the relation between the LLC and meaningful inductive biases in the particular setting described in this post. I expect future SLT to give us meaningful predictions about inductive biases in DL in particular. 

The post is live here.

If we actually had the precision and maturity of understanding to predict this "volume" question, we'd probably (but not definitely) be able to make fundamental contributions to DL generalization theory + inductive bias research. 

Obligatory singular learning theory plug: SLT can and does make predictions about the "volume" question. There will be a post soon by @Daniel Murfet that provides a clear example of this.