arxiv: 2605.10674 · v1 · submitted 2026-05-11 · 💻 cs.LG · cs.AI· cs.CL· cs.SE

Recognition: no theorem link

Step Rejection Fine-Tuning: A Practical Distillation Recipe

Igor Slinko , Ilia Zavidnyi , Egor Bogomolov , Yaroslav Zharov

Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:37 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CLcs.SE

keywords Step Rejection Fine-TuningLLM agentsrejection fine-tuningSWE-benchtrajectory filteringcritic modelstep-level loss maskingdistillation

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The pith

Step Rejection Fine-Tuning masks loss on incorrect steps inside failed trajectories instead of discarding them, raising SWE-bench Verified resolution from 28.5 percent to 32.2 percent.

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

Standard rejection fine-tuning throws away every unsuccessful run when training LLM agents for software tasks. Step Rejection Fine-Tuning keeps those runs but uses a critic model to mark which individual steps inside them are wrong. Training then ignores the loss on the marked steps while the full sequence stays in the context window. The model therefore sees examples of how to recover from mistakes without being trained to repeat them. On SWE-bench Verified this produces a 3.7 percent lift in end-to-end resolution rate, larger than the gain from simply dropping the failed trajectories.

Core claim

Rejection Fine-Tuning discards any trajectory whose final patch fails the tests. Step Rejection Fine-Tuning replaces outright discard with a per-step filter: a critic LLM labels every action in the trajectory as correct or incorrect; the training loss is zeroed out on the incorrect steps while the surrounding tokens remain visible. The resulting gradient signal teaches the policy both to avoid the labeled errors and to continue productively from the last correct prefix.

What carries the argument

A critic LLM that assigns binary correctness labels to each step, combined with selective loss masking that keeps the entire trajectory in the context window.

If this is right

SRFT extracts training value from the large share of partially correct trajectories that standard RFT wastes.
The method raises resolution rate by 3.7 percent to reach 32.2 percent on SWE-bench Verified.
It outperforms the 2.4 percent gain obtained by simply excluding all unresolved trajectories.
Agents learn recovery behavior because the context of a mistake remains visible while its loss contribution is removed.

Where Pith is reading between the lines

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

The same step-level masking could be applied to any long-horizon agent task where partial traces are cheap to collect but full successes are rare.
Accuracy of the critic becomes the dominant bottleneck; replacing the critic with a stronger model or human labels would be a direct next experiment.
Because the method keeps failed trajectories in context, it may reduce the total number of successful examples needed to reach a target performance level.

Load-bearing premise

The critic LLM must correctly identify which steps are wrong so that masking them supplies useful training signal rather than noise.

What would settle it

A controlled run that applies the same critic labels but measures whether resolution rate rises, stays flat, or falls relative to discarding the trajectories outright.

read the original abstract

Rejection Fine-Tuning (RFT) is a standard method for training LLM agents, where unsuccessful trajectories are discarded from the training set. In the context of SWE-bench tasks, this corresponds to filtering out runs where the submitted patch does not pass the tests. However, this approach discards unresolved trajectories, even though they form a large portion of all trajectories for hard tasks and even then may be partially correct. In this work, we propose Step Rejection Fine-Tuning (SRFT) - a practical way to leverage these unresolved trajectories. For this, we employ a critic LLM to assess the correctness of each step in a trajectory. Consequently, during training, we mask the loss for erroneous steps while retaining them in the context window. This way we ensure the model learns to recover from errors without reproducing them. Evaluation on SWE-bench Verified shows that while RFT improves the resolution rate by 2.4% by excluding unresolved trajectories, SRFT improves it by 3.7% by filtering them instead of discarding completely, reaching the total resolution rate of 32.2%.

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

SRFT is a simple practical tweak that keeps unresolved trajectories and masks loss on critic-flagged bad steps, giving a 1.3% extra lift over plain RFT to reach 32.2% on SWE-bench Verified, but the gain rests on an unvalidated critic.

read the letter

The key thing to know is that this paper turns the large pile of failed agent trajectories into partial training data instead of throwing them away. It runs a critic LLM over each step, masks the loss on the ones the critic calls wrong, and keeps the full sequence in context so the model can learn recovery without copying the error. On SWE-bench Verified that produces a 3.7% resolution-rate gain versus 2.4% for standard rejection fine-tuning, landing at 32.2% total. The difference is modest but directly tied to salvaging signal that would otherwise be discarded.

Referee Report

2 major / 1 minor

Summary. The paper proposes Step Rejection Fine-Tuning (SRFT) as a practical improvement to Rejection Fine-Tuning (RFT) for training LLM agents on SWE-bench tasks. RFT discards unsuccessful trajectories entirely, while SRFT retains unresolved trajectories in the context window but uses a critic LLM to label individual steps as correct or erroneous and masks the loss only on the erroneous steps. This is intended to let the model learn recovery from mistakes without reproducing them. On SWE-bench Verified, the authors report that RFT yields a 2.4% resolution-rate gain while SRFT yields a 3.7% gain, reaching 32.2%.

Significance. If the critic labels are sufficiently reliable, SRFT provides a simple, data-efficient way to salvage signal from the large fraction of partially correct but ultimately failing trajectories that RFT throws away. The reported 3.7% absolute lift on a standard benchmark is a concrete, falsifiable empirical result that could be adopted as a lightweight distillation recipe without architectural changes.

major comments (2)

[Abstract] Abstract: the 3.7% resolution-rate improvement (to 32.2%) is presented as the key result, yet no error bars, standard deviations across runs, or statistical significance tests are supplied. Without these, it is impossible to judge whether the 1.3% edge over RFT is robust or could be explained by run-to-run variance.
[Method] Method (critic labeling procedure): the central claim that masking loss on critic-flagged erroneous steps produces useful training signal rests on the untested assumption that the critic LLM correctly identifies step-level errors at high precision and recall. No human agreement study, error analysis on the critic, or ablation that varies critic quality is reported; any systematic mislabeling would directly contaminate the masked loss while still keeping the trajectory in context.

minor comments (1)

[Abstract] Abstract: the phrasing 'filtering unresolved trajectories instead of discarding them completely' is slightly ambiguous; a brief quantitative statement of what fraction of trajectories are retained and what fraction of tokens are masked would help readers gauge the practical difference from RFT.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our work. We appreciate the emphasis on statistical robustness and critic validation, both of which are important for strengthening the claims. We address each major comment below and commit to revisions where appropriate.

read point-by-point responses

Referee: [Abstract] Abstract: the 3.7% resolution-rate improvement (to 32.2%) is presented as the key result, yet no error bars, standard deviations across runs, or statistical significance tests are supplied. Without these, it is impossible to judge whether the 1.3% edge over RFT is robust or could be explained by run-to-run variance.

Authors: We acknowledge that the reported gains are from single training runs and that error bars or multi-seed statistics would better quantify robustness. Due to the substantial compute required for full SWE-bench fine-tuning, we did not perform multiple independent runs in the original experiments. In the revised manuscript we will (i) explicitly state in the abstract and results section that the numbers reflect single runs, (ii) report any observed variance from smaller-scale ablations we have available, and (iii) add a brief discussion of expected run-to-run variance based on prior literature on LLM fine-tuning. We believe this addresses the concern without overstating what the current data support. revision: partial
Referee: [Method] Method (critic labeling procedure): the central claim that masking loss on critic-flagged erroneous steps produces useful training signal rests on the untested assumption that the critic LLM correctly identifies step-level errors at high precision and recall. No human agreement study, error analysis on the critic, or ablation that varies critic quality is reported; any systematic mislabeling would directly contaminate the masked loss while still keeping the trajectory in context.

Authors: The referee is correct that we did not directly validate the critic's step-level accuracy. The primary evidence in the paper is the downstream performance lift of SRFT over RFT, which indirectly suggests the critic provides net-positive signal. For the revision we will add a dedicated subsection that includes: (a) a human agreement study on a random sample of 200 step labels (reporting precision, recall, and Cohen's kappa), (b) an error analysis categorizing the most common critic mistakes, and (c) an ablation replacing the critic with a weaker model to measure sensitivity. These additions will make the reliance on the critic explicit and testable. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical procedure with independent evaluation

full rationale

The paper describes SRFT as a training recipe that retains unresolved trajectories but masks loss on steps labeled erroneous by a critic LLM. The reported gains (RFT +2.4%, SRFT +3.7% to 32.2% resolution on SWE-bench Verified) are presented strictly as measured outcomes of applying this procedure, with no equations, fitted parameters, or derivation steps that reduce to the inputs by construction. No self-citations are invoked as load-bearing premises, and the method does not rename or smuggle in prior results. The evaluation is external to the training signal, so the central claim remains non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that an off-the-shelf or fine-tuned critic LLM can reliably identify erroneous steps; no free parameters or new invented entities are introduced in the abstract.

axioms (1)

domain assumption A critic LLM can accurately assess the correctness of each step in an agent trajectory
This labeling is required to decide which tokens receive masked loss during SRFT training.

pith-pipeline@v0.9.0 · 5505 in / 1255 out tokens · 36531 ms · 2026-05-12T03:37:35.776798+00:00 · methodology

discussion (0)

Reference graph

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