Trendy AI methods rely closely on post-training strategies like supervised fine-tuning (SFT) and reinforcement studying (RL) to adapt basis fashions for particular duties. Nevertheless, a crucial query stays unresolved: do these strategies assist fashions memorize coaching knowledge or generalize to new situations? This distinction is significant for constructing sturdy AI methods able to dealing with real-world variability.
Prior work suggests SFT dangers overfitting to coaching knowledge, making fashions brittle when confronted with new job variants. For instance, an SFT-tuned mannequin would possibly excel at arithmetic issues utilizing particular card values (e.g., treating ‘J’ as 11) however fail if the foundations change (e.g., ‘J’ turns into 10). Equally, RL’s reliance on reward indicators might both encourage versatile problem-solving or reinforce slim methods. Nevertheless, present evaluations usually conflate memorization and true generalization, leaving practitioners unsure about which technique to prioritize. In a modern paper from HKU, UC Berkeley, Google DeepMind, and NYU examine this by evaluating how SFT and RL have an effect on a mannequin’s potential to adapt to unseen rule-based and visible challenges.
They suggest to check generalization in managed settings to isolate memorization from generalization. Researchers designed two duties: GeneralPoints (arithmetic reasoning) and V-IRL (visible navigation). Each duties embrace in-distribution (ID) coaching knowledge and out-of-distribution (OOD) variants to check adaptability:
- Rule-Primarily based Generalization (GeneralPoints, proven in Fig 3):
- Activity: Create equations equal to 24 utilizing 4 numbers from enjoying playing cards.
- Variants: Change card-value guidelines (e.g., ‘J’ = 11 vs. ‘J’ = 10) or card colours (pink vs. blue).
- Aim: Decide if fashions study arithmetic ideas or memorize particular guidelines.
- Visible Generalization (V-IRL, proven in Fig 4):
- Activity: Navigate to a goal location utilizing visible landmarks.
- Variants: Change motion areas (absolute instructions like “north” vs. relative instructions like “flip left”) or take a look at in unseen cities.
- Aim: Assess spatial reasoning impartial of memorized landmarks.
For experiments, the research makes use of Llama-3.2-Imaginative and prescient-11B as the bottom mannequin, making use of SFT first (normal observe) adopted by RL. Key experiments measured efficiency on OOD duties after every coaching part. Let’s now focus on some crucial insights from the paper:
How do SFT and RL Differ in Studying Mechanisms?
- SFT’s Memorization Bias: SFT trains fashions to duplicate right responses from labeled knowledge. Whereas efficient for ID duties, this method encourages sample matching. As an example, if skilled on pink playing cards in GeneralPoints, the mannequin associates coloration with particular quantity assignments. When examined on blue playing cards (OOD), efficiency plummets as a result of it memorizes color-number correlations as an alternative of arithmetic logic. Equally, in V-IRL, SFT fashions memorize landmark sequences however wrestle with new metropolis layouts.
- RL’s Generalization Energy: RL optimizes for reward maximization, which forces fashions to grasp job construction. In GeneralPoints, RL-trained fashions adapt to new card guidelines by specializing in arithmetic relationships relatively than fastened values. For V-IRL, RL brokers study spatial relationships (e.g., “left” means rotating 90 levels) as an alternative of memorizing flip sequences. This makes them sturdy to visible modifications, akin to unfamiliar landmarks.
One other crucial perception is that RL advantages from verification iterations—a number of makes an attempt to resolve a job inside a single coaching step. Extra iterations (e.g., 10 vs. 1) enable the mannequin to discover various methods, enhancing OOD efficiency by +5.99% in some instances.
In efficiency analysis RL outperforms SFT constantly in each duties as proven in Fig 5 & 6:
- Rule-Primarily based Duties:
- RL improved OOD accuracy by +3.5% (GP-L) and +11.0% (V-IRL-L), whereas SFT degraded efficiency by -8.1% and -79.5%, respectively.
- Instance: When card guidelines modified from ‘J=11’ to ‘J=10’, RL fashions adjusted equations utilizing the brand new values, whereas SFT fashions reused invalid memorized options.
- Visible Duties:
- RL boosted OOD efficiency by +17.6% (GP-VL) and +61.1% (V-IRL-VL), whereas SFT dropped by -9.9% and -5.6%.
- In V-IRL, RL brokers navigated unseen cities by recognizing spatial patterns, whereas SFT failed because of reliance on memorized landmarks.
The research additionally means that SFT is critical to initialize fashions for RL. With out SFT, RL struggles as a result of the bottom mannequin lacks primary instruction-following abilities. Nevertheless, overly-tuned SFT checkpoints hurt RL’s adaptability, the place RL couldn’t get better OOD efficiency after extreme SFT. Nevertheless, the researchers make clear that their findings—particular to the Llama-3.2 spine mannequin—don’t battle with earlier work akin to DeepSeekAI et al. (2025), which proposed that SFT might be omitted for downstream RL coaching when utilizing different base architectures.
In conclusion, this research demonstrates a transparent trade-off: SFT excels at becoming coaching knowledge however falters underneath distribution shifts, whereas RL prioritizes adaptable, generalizable methods. For practitioners, this means that RL ought to observe SFT—however solely till the mannequin achieves primary job competence. Over-reliance on SFT dangers “locking in” memorized patterns, limiting RL’s potential to discover novel options. Nevertheless, RL isn’t a panacea; it requires cautious tuning (e.g., verification steps) and balanced initialization.
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Vineet Kumar is a consulting intern at MarktechPost. He’s at the moment pursuing his BS from the Indian Institute of Expertise(IIT), Kanpur. He’s a Machine Studying fanatic. He’s enthusiastic about analysis and the most recent developments in Deep Studying, Pc Imaginative and prescient, and associated fields.