arxiv: 2501.17161 · v2 · submitted 2025-01-28 · 💻 cs.AI · cs.CV· cs.LG

Recognition: 2 theorem links

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Tianzhe Chu , Yuexiang Zhai , Jihan Yang , Shengbang Tong , Saining Xie , Dale Schuurmans , Quoc V. Le , Sergey Levine

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Yi Ma

Authors on Pith no claims yet

Pith reviewed 2026-05-12 21:25 UTC · model grok-4.3

classification 💻 cs.AI cs.CVcs.LG

keywords supervised fine-tuningreinforcement learninggeneralizationmemorizationfoundation modelspost-trainingarithmetic reasoningvisual navigation

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The pith

Reinforcement learning enables foundation models to generalize to unseen variants while supervised fine-tuning leads to memorization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates the distinct effects of supervised fine-tuning and reinforcement learning during the post-training of foundation models on their ability to generalize. Using a new arithmetic card game called GeneralPoints and the V-IRL navigation setting, it tests performance on rule changes and visual changes not seen during training. The key finding is that reinforcement learning with outcome-based rewards allows models to adapt to these new situations in both text and vision, while supervised fine-tuning makes models rely on memorizing specific training instances. This distinction matters for building models that can handle novel real-world scenarios rather than repeating what they saw in training. The analysis also shows that reinforcement learning boosts visual recognition skills, and that supervised fine-tuning is still required first to keep outputs in a consistent format so reinforcement learning can succeed.

Core claim

Reinforcement learning, particularly with outcome-based rewards, generalizes across both rule-based textual and visual variants in tasks like GeneralPoints and V-IRL. In contrast, supervised fine-tuning tends to memorize training data and struggles with out-of-distribution scenarios. Reinforcement learning enhances underlying visual recognition capabilities, but supervised fine-tuning is essential to stabilize output formats before reinforcement learning can achieve its gains.

What carries the argument

Outcome-based reward signals in reinforcement learning applied after supervised fine-tuning, tested on the GeneralPoints card game for arithmetic reasoning and V-IRL for real-world navigation.

If this is right

RL-trained models perform well on unseen rule variants in text and new visual conditions without additional training.
SFT alone results in poor generalization to new scenarios in multi-modal tasks.
A two-stage process of SFT followed by RL combines format stability with generalization ability.
RL training improves core perceptual skills such as visual recognition that support broader task performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

These results point to the value of incorporating outcome-based RL in post-training pipelines for better adaptability in changing environments.
The pattern may extend to other foundation model applications like language understanding or decision making where rules can vary.
Developers could experiment with different reward designs to see if they further enhance generalization beyond the tested games and navigation tasks.

Load-bearing premise

The performance gaps observed between SFT and RL on GeneralPoints and V-IRL truly indicate differences in generalization ability rather than being specific to the design of those two test environments.

What would settle it

Training new models with RL on GeneralPoints or V-IRL and then testing them on a completely different task with novel rules and visuals, finding no generalization advantage over SFT models, would falsify the central claim.

read the original abstract

Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization capabilities remain unclear. This paper studies the difference between SFT and RL on generalization and memorization, focusing on text-based rule variants and visual variants. We introduce GeneralPoints, an arithmetic reasoning card game, and adopt V-IRL, a real-world navigation environment, to assess how models trained with SFT and RL generalize to unseen variants in both textual and visual domains. We show that RL, especially when trained with an outcome-based reward, generalizes across both rule-based textual and visual variants. SFT, in contrast, tends to memorize training data and struggles to generalize out-of-distribution scenarios. Further analysis reveals that RL improves the model's underlying visual recognition capabilities, contributing to its enhanced generalization in the visual domain. Despite RL's superior generalization, we show that SFT remains essential for effective RL training; SFT stabilizes the model's output format, enabling subsequent RL to achieve its performance gains. These findings demonstrates the capability of RL for acquiring generalizable knowledge in complex, multi-modal tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

RL with outcome rewards generalizes to new rule and visual variants on their benchmarks while SFT mostly memorizes, and SFT is still required to stabilize outputs for RL to work.

read the letter

RL generalizes to new rule and visual variants while SFT sticks to memorizing the training set. That's the core finding from their experiments on the GeneralPoints card game and V-IRL navigation tasks. They created GeneralPoints as an arithmetic reasoning game and tested both text-based rule changes and visual versions, plus the navigation environment. The results show outcome-reward RL transfers across these shifts, and RL training also improves basic visual recognition in the navigation setting. They also demonstrate that SFT is still essential because it forces the model into the right output format so RL can then deliver gains. These are practical observations for anyone doing post-training on foundation models. The head-to-head comparison on the same tasks with controlled variants is the useful part, and the SFT-for-stability point is something people can apply directly. The soft spot is the newness of the benchmarks. The variants might not cleanly separate memorization from generalization if the rule or visual changes happen to hit surface patterns harder for SFT or if RL simply benefits from its exploration tolerance under the reward. Since the environments were built for this study, small unintended differences in difficulty or required computation could produce the same pattern. More runs, error bars, and explicit checks on variant construction would tighten that. This is aimed at researchers working on post-training for multi-modal models, especially those focused on reasoning or navigation tasks. Readers will get a clear empirical contrast and some actionable takeaways on when to use each method. The paper has enough new data and a straightforward message to deserve peer review, where the main questions will be about benchmark controls and statistical robustness.

Referee Report

3 major / 2 minor

Summary. The paper claims that RL (especially outcome-based) generalizes better than SFT to out-of-distribution rule-based textual and visual variants in the newly introduced GeneralPoints arithmetic card game and the adopted V-IRL navigation environment. SFT is said to memorize training data and fail on OOD cases, while RL also improves underlying visual recognition; SFT is nevertheless required to stabilize output formats before effective RL training.

Significance. If the central empirical distinction holds after addressing setup details, the work would usefully separate the memorization/generalization roles of SFT versus RL in multi-modal post-training and demonstrate that outcome rewards can yield rule-like behavior across modalities. The introduction of GeneralPoints and controlled variants of V-IRL supplies concrete testbeds that future studies could reuse.

major comments (3)

[Experiments / Results sections] The manuscript provides no details on the number of training runs, random seeds, statistical significance tests, or error bars for the reported performance gaps between SFT and RL on GeneralPoints and V-IRL variants. Without these, it is impossible to judge whether the observed generalization advantage is robust or could be explained by variance or implementation differences.
[GeneralPoints and V-IRL environment descriptions] The claim that performance differences reflect acquisition of generalizable rules rather than RL exploiting the outcome reward's tolerance for exploration (while SFT overfits surface patterns) rests on the untested assumption that the textual and visual variants in GeneralPoints and V-IRL alter only the intended rule or visual feature. No ablations are described that hold reward structure, visual complexity, or required computation constant while changing only the generalization target.
[Analysis of visual recognition] The additional assertion that RL improves underlying visual recognition in V-IRL is not separated from policy optimization effects; the paper does not report auxiliary recognition metrics or controlled probes that would show gains independent of the navigation policy.

minor comments (2)

[Abstract] Abstract contains a subject-verb agreement error: 'These findings demonstrates' should read 'These findings demonstrate'.
[Figures and tables] All result tables and figures should include error bars, exact sample sizes, and explicit comparison of SFT-only, RL-only, and SFT-then-RL pipelines.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments on our work. We provide detailed responses to each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Experiments / Results sections] The manuscript provides no details on the number of training runs, random seeds, statistical significance tests, or error bars for the reported performance gaps between SFT and RL on GeneralPoints and V-IRL variants. Without these, it is impossible to judge whether the observed generalization advantage is robust or could be explained by variance or implementation differences.

Authors: We agree with this observation. The original manuscript omitted these details due to space constraints, but we recognize their importance. In the revised version, we will report the number of independent training runs (conducted with different random seeds), include error bars (standard deviations) on all performance figures, and add statistical significance tests (such as paired t-tests) to confirm that the generalization gaps between SFT and RL are significant. revision: yes
Referee: [GeneralPoints and V-IRL environment descriptions] The claim that performance differences reflect acquisition of generalizable rules rather than RL exploiting the outcome reward's tolerance for exploration (while SFT overfits surface patterns) rests on the untested assumption that the textual and visual variants in GeneralPoints and V-IRL alter only the intended rule or visual feature. No ablations are described that hold reward structure, visual complexity, or required computation constant while changing only the generalization target.

Authors: The variants were designed with the explicit goal of isolating changes to the rule or visual feature. For GeneralPoints, textual variants modify the arithmetic operations while keeping card values, game mechanics, and the outcome-based reward identical. Similarly for V-IRL visual variants. We will revise the environment sections to provide more explicit descriptions of these controls and how they maintain constancy in other factors. While we did not perform additional ablations beyond the main experiments, the controlled construction supports our claims; we can add a paragraph discussing this design choice. revision: partial
Referee: [Analysis of visual recognition] The additional assertion that RL improves underlying visual recognition in V-IRL is not separated from policy optimization effects; the paper does not report auxiliary recognition metrics or controlled probes that would show gains independent of the navigation policy.

Authors: Our analysis in the manuscript includes probes on visual element recognition in V-IRL scenes after training. To address the separation from policy optimization, we will add explicit auxiliary metrics, such as accuracy on a visual recognition task using frozen policy models or separate evaluation on image classification of navigation-relevant objects. This will be included in the revised analysis section to demonstrate gains independent of the full navigation policy. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results on new environments

full rationale

The paper is an empirical study that introduces GeneralPoints and adopts V-IRL, then reports direct experimental comparisons of SFT versus RL on in-distribution and out-of-distribution textual and visual variants. No mathematical derivation, prediction, or first-principles claim is present that reduces by construction to fitted parameters, self-definitions, or self-citations. Generalization claims rest on measured performance gaps rather than any re-labeling of inputs as outputs. The work is self-contained against external benchmarks via explicit task variants and metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical comparison study; no free parameters, invented entities, or new mathematical axioms are introduced in the abstract. It relies on standard domain assumptions about measuring generalization via held-out variants.

axioms (1)

domain assumption Performance differences on held-out variants of GeneralPoints and V-IRL indicate true generalization rather than benchmark-specific effects.
The paper uses these environments as proxies for broader capabilities without additional validation of their representativeness.

pith-pipeline@v0.9.0 · 5536 in / 1342 out tokens · 52948 ms · 2026-05-12T21:25:13.756846+00:00 · methodology

discussion (0)

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Reference graph

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forward()

Move forward until the destination Café de la Presse is on your right. [Current observation] You observe a 2x2 grid of street view images with the following headings: [front, right back, left] You need to identify if any of the landmarks in the instruction are visible in the street view grid. [Action space] - "forward()": indicates moving forward for 1 st...

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[15]

First, turn left to face east

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Move forward until you reach the next intersection where Hotel 32One is on your right behind

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Move forward until you reach the next intersection where Dragon Gate Chinatown SF is on your right front

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forward()

Move forward until the destination Café de la Presse is on your right. [Action space] - "forward()": indicates moving forward for 1 step; - "turn_direction(x)": indicates turn direction to the target heading, where x ∈['north', 'northeast', 'east', 'southeast', 'south', 'southwest', 'west', 'northwest']; - "stop()": indicates the navigation is finished; [...

work page 2017
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First, turn right to face northwest

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Move forward until you reach next intersection where Korean War Memorial is on your left

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Turn left to face southwest

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Move forward until you reach next intersection where Korean War Memorial is on your left behind

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[25]

Turn right to face north

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[27]

Turn left to face east

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[29]

Turn left to face north

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[31]

Turn right to face east

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[32]

Move forward until you reach next intersection

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[33]

Turn left to face northeast

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[34]

Move forward until you reach next intersection where 9/11 Memorial & Museum is on your left

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[35]

Turn right to face northwest

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forward()

Move forward until you reach destination where The destination 9/11 Memorial & Museum is on your right front. [Action space] "forward()": indicates moving forward one step "turn_direction(x)": indicates adjust the ego agent direction towards x direction. x could be any following [’left’, ’right’, ’slightly left’, ’slightly right’] "stop()": indicates the ...

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