AI engineers usually chase efficiency by scaling up LLM parameters and knowledge, however the pattern towards smaller, extra environment friendly, and better-focused fashions has accelerated. 

The Phi-4 fine-tuning methodology is the cleanest public instance of a coaching method that smaller enterprise groups can copy. It exhibits how a rigorously chosen dataset and fine-tuning technique could make a 14B mannequin compete with a lot bigger ones.

The Phi-4 mannequin was skilled on simply 1.4 million rigorously chosen prompt-response pairs. As a substitute of brute power, the Microsoft Phi-4 analysis group targeted on “teachable” examples on the fringe of the mannequin’s talents and rigorous knowledge curation. 

The Phi-4 reasoning good knowledge playbook demonstrates how strategic knowledge curation with replicable SFT and RL can elevate a 14B mannequin past a lot bigger counterparts.

Why Phi-4 stands aside

Smaller reasoning fashions, resembling OpenAI’s o1-mini and Google’s Gemma, have gotten extra widespread, and fashions like Alibaba’s Qwen3 (8B and 14B) are seeing extensive adoption throughout use instances. That adoption is essential, but it surely doesn’t displace the worth of Phi-4 as an experimental proof: Phi-4 was designed as a testbed for a data-first coaching methodology, and its documentation reads like a sensible knowledge playbook for groups that need to replicate that method.

The Phi-4 group has shared a repeatable SFT playbook that features a 1.4-million-prompt response set. It’s constructed round “teachable” edge examples, questions which can be neither too simple nor too troublesome, chosen to push the mannequin’s reasoning. Every subject, resembling math or code, is tuned individually after which mixed with artificial rewrites that flip complicated duties into types that may be checked routinely. 

The paper outlines the information choice and filtering course of in sufficient element for smaller groups to breed it with open-source fashions and evaluators. For enterprise groups, that degree of transparency turns a analysis consequence right into a sensible, copyable coaching recipe they will implement and measure shortly.

The information-first philosophy: Why much less will be extra

Conventional approaches to LLM reasoning have usually relied on scaling datasets massively to encourage generalization. Phi-4 reasoning takes a distinct path, exhibiting that rigorously curated knowledge can obtain comparable and even higher outcomes with far much less.

The group assembled a dataset masking STEM, coding, and security. Regardless of its small measurement, it outperformed fashions skilled on orders of magnitude extra knowledge. 

In benchmarks, the 14B Phi-4 reasoning mannequin outperformed OpenAI’s o1-mini and DeepSeek’s 70B distilled mannequin throughout most reasoning duties, and approached the total DeepSeek-R1 (671B) on difficult math (AIME) questions. 

With simply 14 billion parameters, Phi-4 reasoning delivers the next outcomes when in comparison with different main fashions:

Benchmark (job)	Phi-4 reasoning	Comparability mannequin (measurement)	Comparability rating	Date / Supply
AIME 2024 (math olympiad)	75.3%	o1-mini	63.6%	Microsoft Phi-4 mannequin card (Apr 2025). (Hugging Face)
AIME 2025 (math olympiad)	62.9%	DeepSeek-R1-Distill-70B	51.5%	Microsoft Phi-4 mannequin card (April 2025). (Hugging Face)
OmniMath	76.6%	DeepSeek-R1-Distill-70B	63.4%	Microsoft Phi-4 mannequin card (April 2025). (Hugging Face)
GPQA-Diamond (graduate-level science)	65.8%	o1-mini	60.0%	Microsoft Phi-4 mannequin card (April 2025). (Hugging Face)
OmniMath (identical benchmark, totally different comparability)	76.6%	Claude-3.7-Sonnet	54.6%	Microsoft Phi-4 mannequin card (April 2025). (Hugging Face)

Desk: Phi-4 reasoning efficiency throughout benchmarks in comparison with different fashions. Supply: Microsoft

The important thing to that is filtering for high quality over amount. A lot of the generic knowledge is both too simple (the bottom mannequin already is aware of it) or too arduous (no studying sign). The Phi-4 group explicitly discards such examples. “Given the robust baseline reasoning capabilities of Phi-4, many preliminary seed questions are already dealt with competently,” they notice. “To make additional studying impactful, we particularly goal seeds located on the edge of Phi-4’s present talents.” 

In observe, they depend on LLM-based analysis. For every candidate query, a robust reference mannequin (like GPT-4) generates an “reply key,” and the solutions from weaker fashions are in contrast. If the weaker mannequin disagrees sufficient, it signifies a teachable hole. These questions are retained, whereas trivially solved or totally unsolvable questions are dropped. 

For instance, a easy arithmetic drawback is likely to be dropped (too simple), and a particularly obscure theorem proof is likely to be dropped (too arduous) as effectively. However a reasonably difficult geometry drawback that Phi-4 will get mistaken is included.

This “candy spot” method ensures each instance forces the mannequin to stretch its reasoning. By specializing in multi-step issues somewhat than rote recall, they pack most studying into 1.4M examples. 

Because the authors clarify, coaching on these rigorously chosen seeds “results in broad generalization throughout each reasoning-specific and general-purpose duties.” In impact, Phi-4 reasoning demonstrates that clever knowledge choice can outperform brute power scaling. 

Unbiased area optimization

Phi-4 reasoning’s knowledge are grouped by area (math, coding, puzzles, security, and so on.). Slightly than mixing all the things without delay, the group tunes every area’s combine individually after which merges them. 

This depends on an “additive property”: Optimizing math knowledge in isolation and code knowledge in isolation yields weights that, when concatenated, nonetheless give positive factors in each areas. In observe, they first tuned the maths dataset to saturation on math benchmarks, then did the identical for code, and at last merely added the code knowledge into the maths recipe. The consequence was improved efficiency on each math and coding duties, with out retraining from scratch.

This modular method affords clear sensible benefits. This implies a small group can first refine simply the maths dataset, obtain robust math efficiency, after which later add the coding knowledge with out redoing the maths tuning.

Nonetheless, the Phi-4 authors warning that scaling this technique to many domains stays an open query. Whereas the method “labored very effectively” for his or her math+code combine, they notice, “it isn’t identified whether or not this technique can scale to dozens or a whole bunch of domains,” a course they acknowledge as a priceless space for future analysis. In brief, the additive technique is efficient, however increasing into new domains should be approached rigorously, as it could introduce unexpected interactions.

Regardless of potential pitfalls, the additive technique proved efficient in Phi-4 reasoning. By treating every area independently, the group averted complicated joint optimization and narrowed the search area for knowledge mixtures. This method permits incremental scaling of domains. Groups can start by tuning the maths SFT, then incorporate the code dataset, and later develop to further specialised duties, all whereas sustaining prior efficiency positive factors. 

It is a sensible benefit for resource-constrained groups. As a substitute of requiring a big group of specialists to handle a fancy, multi-domain dataset, a small group can deal with one knowledge silo at a time.

Artificial knowledge transformation

Some reasoning issues, resembling summary proofs or artistic duties, are troublesome to confirm routinely. But automated verification (for RL reward shaping) could be very priceless. Phi-4 reasoning tackled this by remodeling arduous prompts into easier-to-check types. 

For instance, the group rewrote a subset of coding issues as phrase puzzles or transformed some math issues to have concise numeric solutions. These “artificial seed knowledge” protect the underlying reasoning problem however make correctness simpler to check. Consider it as giving the mannequin a simplified model of the riddle that also teaches the identical logic. 

This engineering hack allows downstream RL to make use of clear reward alerts on duties that might in any other case be too open-ended. 

Right here’s an instance of artificial knowledge transformation:

Uncooked internet knowledge	Artificial knowledge
On the perimeters AB and BC of triangle ABC, factors M and N are taken, respectively. It seems that the perimeter of △AMC is the same as the perimeter of △CNA, and the perimeter of △ANB is the same as the perimeter of △CMB. Show that △ABC is isosceles.	ABC is a triangle with AB=13 and BC=10. On the perimeters AB and BC of triangle ABC, factors M and N are taken, respectively. It seems that the perimeter of △AMC is the same as the perimeter of △CNA, and the perimeter of △ANB is the same as the perimeter of △CMB. What’s AC?

Desk: Rewriting seed knowledge from the net (left) into verifiable artificial questions for SFT and RL (proper). Supply: Microsoft

Be aware that by assigning numeric values (AB=13, BC=10) and asking “What’s AC?”, the reply turns into a single quantity, which will be simply checked for correctness.

Different groups have utilized comparable domain-specific methods. For instance, chemistry LLMs like FutureHouse’s ether0 mannequin generate molecules beneath strict pKa or structural constraints, utilizing crafted reward capabilities to make sure legitimate chemistry. 

In arithmetic, the Kimina-Prover model by Numina interprets natural-language theorems into the Lean formal system, so reinforcement studying can confirm right proofs. These examples spotlight how artificial augmentation, when paired with verifiable constraints, can push fashions to carry out effectively in extremely specialised domains.

In sensible phrases, engineers ought to embrace artificial knowledge however maintain it grounded. Heuristics like “convert to numeric solutions” or “decompose a proof into checkable steps” could make coaching safer and extra environment friendly. On the identical time, preserve a pipeline of actual (natural) issues as effectively, to make sure breadth. 

The secret is steadiness. Use artificial transformations to unlock troublesome verification issues, however don’t depend on them completely. Actual-world range nonetheless issues. Following this method, the mannequin is guided towards a clearly outlined, discrete goal.

Listed here are some outcomes on Phi-4 reasoning fashions:

Sensible implementation for enterprises

AI groups seeking to apply Phi-4 reasoning’s insights can observe a collection of concrete steps to implement the method successfully.

Figuring out the mannequin’s edge

Detect your mannequin’s “edge” by figuring out the place the bottom LLM struggles. A method is to make use of its confidence or settlement scores. For instance, generate a number of solutions per immediate (utilizing a device like Hugging Face’s vLLM for quick sampling) and see the place consensus breaks. These prompts on the margin of confidence are your teachable examples. By specializing in these low-confidence questions somewhat than the questions it already will get proper, you guarantee every new instance is value studying.

Isolating domains for focused tuning

Tune one area at a time somewhat than mixing all knowledge genres upfront. Choose the highest-value area to your app (math, code, authorized, and so on.) and craft a small SFT dataset for simply that. Iterate on the combination (balancing issue, supply sorts, and so on.) till efficiency saturates on domain-specific benchmarks. Then freeze that blend and add the subsequent area. This modular tuning follows Phi-4 reasoning’s “additive” technique. It avoids cross-talk because you protect positive factors in area A at the same time as you enhance area B.

Increasing with artificial augmentation

Leverage artificial augmentation when gold-standard solutions are scarce or unverifiable. For example, if you might want to train a proof assistant however can’t autocheck proofs, remodel them into arithmetic puzzles or shorter proofs that may be verified. Use your LLM to rewrite or generate these variants (Phi-4 used this to show complicated phrase issues into numeric ones). 

Artificial augmentation additionally allows you to develop knowledge cheaply. After getting a validated small set, you’ll be able to “multiply” it by having the LLM generate paraphrases, variations, or intermediate reasoning steps.

Scaling by a two-phase technique

Use a two-phase coaching technique that begins with exploration adopted by scaling. In Section 1 (exploration), run brief fine-tuning experiments on a targeted dataset (e.g., one area) with restricted compute. Monitor just a few key metrics (benchmarks or held-out duties) every run. Quickly iterate hyperparameters and knowledge mixes. 

The Phi-4 paper demonstrates that this hastens progress, as small experiments helped the group uncover a strong recipe earlier than scaling up. Solely when you see constant positive factors do you progress to Section 2 (scaling), the place you mix your verified recipes throughout domains and practice longer (in Phi-4’s case, ~16 billion tokens). Though this stage is extra compute-intensive, the chance is considerably lowered by the prior experimentation.

Monitor for set off factors resembling a big uplift on validation duties or secure metric developments. When these seem, it’s time to scale. If not, refine the recipe extra first. This disciplined two-phase loop saves assets and retains the group agile.

In observe, many groups at Hugging Face and elsewhere have adopted comparable recommendation. For instance, whereas creating conversational mannequin SmolLM2, the group seen poor chat efficiency in Section 1. They then generated ~500K artificial multi-turn dialogues and re-trained, which “considerably improved each downstream efficiency and its general ‘vibes,’” as one researcher reviews. This represents a concrete win, achieved by a focused artificial knowledge injection primarily based on an preliminary suggestions loop.

How to do that now

Right here’s a easy guidelines that you would be able to observe to place these concepts into motion.

1. Choose a goal area/job. Select one space (e.g., math, coding, or a selected software) the place you want higher efficiency. This retains the challenge targeted.

2. Accumulate a small seed dataset. Collect, say, just a few thousand immediate–reply pairs in that area from present sources (textbooks, GitHub, and so on.).

3. Filter for edge-of-ability examples. Use a robust mannequin (e.g., GPT-4) to create a solution key for every immediate. Run your base mannequin on these prompts. Hold examples that the bottom mannequin usually misses, discard ones it already solves or is hopeless on. This yields “teachable” examples.

4. High-quality-tune your mannequin (Section 1). Run a brief SFT job on this curated knowledge. Monitor efficiency on a held-out set or benchmark. Iterate: Refine the information combine, take away simple questions, add new teachable ones, till positive factors taper off.

5. Add artificial examples if wanted. If some ideas lack auto-verifiable solutions (like lengthy proofs), create easier numeric or single-answer variants utilizing your LLM. This provides clear rewards for RL. Hold a steadiness with actual issues.

6. Increase to the subsequent area. As soon as one area is tuned, “freeze” its dataset. Choose a second high-value area and repeat steps 3 to five to tune that knowledge combine. Lastly, merge the information for each domains, and do a last longer coaching run (Section 2).

7. Monitor benchmarks rigorously. Use a constant analysis methodology (like  majority-voting runs) to keep away from deceptive outcomes. Solely proceed to a full-scale coaching if small experiments present clear enhancements.

Limits and trade-offs

Regardless of the effectiveness of the Phi-4 coaching technique, a number of limitations and sensible issues stay. One key problem is area scaling. Whereas Phi-4’s additive technique labored effectively for math and code, it has but to be confirmed throughout many domains. The authors acknowledge that it stays an open query whether or not this method can scale easily to dozens of matters. 

One other concern is the usage of artificial knowledge. Relying too closely on artificial rewrites can cut back the variety of the dataset, so it’s essential to take care of a steadiness between actual and artificial examples to protect the mannequin's capability to purpose successfully. 

Lastly, whereas the repeatable SFT technique helps cut back computational prices, it doesn’t get rid of the necessity for considerate curation. Although the method is extra environment friendly than brute-force scaling, it nonetheless requires cautious knowledge choice and iteration.

Classes from Phi-4

The Phi-4 reasoning story is obvious: Larger isn’t at all times higher for reasoning fashions. As a substitute of blindly scaling, the group requested the place studying occurs and engineered their knowledge to hit that candy spot. They present that “the advantage of cautious knowledge curation for supervised fine-tuning extends to reasoning fashions.” In different phrases, with a sensible curriculum, you’ll be able to squeeze stunning functionality out of modest fashions.

For engineers, the takeaway is actionable. You don’t want a billion-dollar cluster or an infinite web crawl to enhance reasoning. For resource-strapped groups, that is excellent news, as a cautious knowledge technique allows you to punch above your weight.

Phi-4 reasoning proves that systematic knowledge and coaching design, not sheer parameter depend, drives superior reasoning. Specializing in teachable knowledge and iterative tuning, even a 14B mannequin surpassed a lot bigger rivals. For AI groups immediately, this affords a sensible blueprint. Refine the information, iterate quick, and scale solely when the alerts are proper. These steps can unlock breakthrough reasoning efficiency with out breaking the financial institution.

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