RIFT: Repurposing Negative Samples via Reward-Informed Fine-Tuning
Pith reviewed 2026-05-16 14:31 UTC · model grok-4.3
The pith
RIFT improves LLM alignment by reweighting training losses with rewards to learn from negative samples instead of discarding them.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
RIFT repurposes negative trajectories by reweighting the loss with scalar rewards and pairs this with a stabilized formulation to maintain numerical robustness, enabling the use of mixed-quality self-generated data for alignment and yielding consistent gains over rejection sampling fine-tuning on mathematical tasks.
What carries the argument
The stabilized reward-reweighted loss formulation that integrates positive and negative model outputs without causing unbounded values.
If this is right
- Alignment can proceed with all model outputs instead of requiring only expert or positive data.
- Data efficiency increases because negative samples contribute to learning rather than being thrown away.
- The stabilized formulation supports reliable optimization across different base models on reasoning tasks.
- RIFT serves as a direct substitute for rejection sampling fine-tuning when self-generated data is available.
Where Pith is reading between the lines
- If reward signals remain reliable, the approach could support repeated self-improvement cycles without external data.
- The same reweighting idea might apply to non-math domains once suitable reward models exist.
- Lower dependence on curated expert data could reduce costs in large-scale alignment pipelines.
Load-bearing premise
That multiplying the loss by scalar rewards on negative trajectories, when combined with stabilization, will drive genuine improvement without introducing bias or causing optimization collapse.
What would settle it
An experiment applying RIFT to a base model on a standard math benchmark where final performance is equal to or worse than rejection sampling fine-tuning, or where training loss becomes unstable.
read the original abstract
While Supervised Fine-Tuning (SFT) and Rejection Sampling Fine-Tuning (RFT) are standard for LLM alignment, they either rely on costly expert data or discard valuable negative samples, leading to data inefficiency. To address this, we propose Reward Informed Fine-Tuning (RIFT), a simple yet effective framework that utilizes all self-generated samples. Unlike the hard thresholding of RFT, RIFT repurposes negative trajectories, reweighting the loss with scalar rewards to learn from both the positive and negative trajectories from the model outputs. To overcome the training collapse caused by naive reward integration, where direct multiplication yields an unbounded loss, we introduce a stabilized loss formulation that ensures numerical robustness and optimization efficiency. Extensive experiments on mathematical benchmarks across various base models show that RIFT consistently outperforms RFT. Our results demonstrate that RIFT is a robust and data-efficient alternative for alignment using mixed-quality, self-generated data.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript proposes Reward-Informed Fine-Tuning (RIFT) for LLM alignment. Unlike SFT (which requires expert data) or RFT (which discards negative samples via hard thresholding), RIFT reweights the supervised loss on all self-generated trajectories using scalar rewards. To prevent training collapse from unbounded loss under naive reward multiplication, the authors introduce a stabilized loss formulation. Experiments on mathematical benchmarks across base models are reported to show consistent outperformance over RFT, establishing RIFT as a data-efficient alternative that learns from mixed-quality self-generated data.
Significance. If the empirical claims hold with proper controls, the work would be significant for improving data efficiency in alignment: it turns the common problem of negative samples into an asset rather than discarding them. The stabilized loss addresses a concrete numerical issue in reward-weighted objectives. Strengths include the explicit handling of the unbounded-loss pathology and the focus on self-generated data, which aligns with practical deployment constraints.
major comments (2)
- [Abstract] Abstract: the central claim of 'consistent outperformance' on mathematical benchmarks is stated without any quantitative results, error bars, ablation tables, dataset sizes, or base-model details. This leaves the primary empirical support for the method unverified in the provided summary and directly weakens the data-efficiency conclusion.
- [Method] Stabilized loss formulation (described in the method): while the text correctly notes that naive reward multiplication yields unbounded loss, the formulation is not shown to include explicit reward normalization or variance-aware scaling. When reward variance across self-generated trajectories is high, the gradient can still be dominated by a few high-reward positives or push mass toward mediocre negatives, risking the bias or collapse the stabilization is meant to prevent.
minor comments (1)
- [Method] Notation for the stabilized loss should be defined with an explicit equation number and compared term-by-term to the naive form to clarify the numerical safeguard.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will incorporate revisions to strengthen the abstract and clarify the loss formulation.
read point-by-point responses
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Referee: [Abstract] Abstract: the central claim of 'consistent outperformance' on mathematical benchmarks is stated without any quantitative results, error bars, ablation tables, dataset sizes, or base-model details. This leaves the primary empirical support for the method unverified in the provided summary and directly weakens the data-efficiency conclusion.
Authors: We agree that the abstract would be strengthened by including specific quantitative details. In the revised version, we will update the abstract to report key metrics such as average accuracy gains over RFT (with error bars), the base models evaluated, dataset sizes used for self-generation, and references to the main experimental tables. This will make the empirical support for data efficiency more verifiable while preserving the abstract's brevity. revision: yes
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Referee: [Method] Stabilized loss formulation (described in the method): while the text correctly notes that naive reward multiplication yields unbounded loss, the formulation is not shown to include explicit reward normalization or variance-aware scaling. When reward variance across self-generated trajectories is high, the gradient can still be dominated by a few high-reward positives or push mass toward mediocre negatives, risking the bias or collapse the stabilization is meant to prevent.
Authors: We appreciate this observation on potential gradient issues under high reward variance. Our stabilized loss bounds the objective to avoid unbounded growth, but we acknowledge it does not explicitly detail normalization in the current text. We will revise the method section to incorporate reward normalization (e.g., per-batch standardization) and variance-aware scaling, along with a brief analysis or ablation demonstrating improved gradient stability. This addresses the referee's concern without altering the core contribution. revision: yes
Circularity Check
No circularity: RIFT loss is an explicit, non-reductive proposal
full rationale
The paper defines RIFT by introducing an explicit reweighted loss on self-generated trajectories plus a stabilization term to bound the objective. No equation reduces to a fitted parameter renamed as prediction, no uniqueness theorem is imported from self-citation, and no ansatz is smuggled via prior work. The derivation chain is self-contained: the stabilized formulation is stated directly to solve the unbounded-loss problem identified in the abstract, and performance claims rest on empirical benchmarks rather than tautological equivalence to inputs.
Axiom & Free-Parameter Ledger
invented entities (1)
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Stabilized loss formulation
no independent evidence
discussion (0)
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