Self-Execution Simulation Improves Coding Models

Felix Kreuk; Gal Cohen; Gallil Maimon; Michael Hassid; Ori Yoran; Pierre Chambon; Yossi Adi

arxiv: 2604.03253 · v1 · submitted 2026-03-11 · 💻 cs.CL · cs.LG

Self-Execution Simulation Improves Coding Models

Gallil Maimon , Ori Yoran , Felix Kreuk , Michael Hassid , Gal Cohen , Pierre Chambon , Yossi Adi This is my paper

Pith reviewed 2026-05-15 13:34 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords self-execution simulationcode generationLLMscompetitive programmingreinforcement learningsupervised fine-tuningself-verificationself-fixing

0 comments

The pith

Code LLMs improve at competitive programming by learning to simulate their own execution step by step.

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

The paper shows that code-generating language models can be trained to simulate program execution in natural language, step by step. This training combines supervised fine-tuning on execution traces and grounded explanations with reinforcement learning that uses verifiable rewards. The resulting models perform self-verification across candidate solutions and iterative self-fixing by simulating test runs. A sympathetic reader would care because the approach directly targets the common failure of LLMs to accurately predict how their own generated code behaves on inputs.

Core claim

By training on natural language execution traces and textual explanations grounded in true execution, then adding reinforcement learning with verifiable rewards, code LLMs acquire the ability to predict outputs from code and inputs and to solve competitive programming tasks using either ground-truth or self-predicted execution feedback, enabling self-verification and iterative self-fixing that yields consistent gains over standard reasoning methods.

What carries the argument

Self-execution simulation: the model learns to output step-by-step natural language traces of program behavior and then uses those traces (ground-truth or self-generated) as feedback for verifying and repairing candidate solutions.

If this is right

Models can perform self-verification over multiple candidate solutions using their own simulated execution.
Iterative self-fixing becomes possible by repeatedly simulating test execution and revising outputs.
Two complementary training objectives, output prediction and task solving with execution feedback, together drive the gains.
Ablations confirm that the execution-simulation component is responsible for the observed improvements.

Where Pith is reading between the lines

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

If execution simulation generalizes beyond the training distribution, the same technique could be applied to domains that require step-by-step verification such as mathematical proof generation.
Reliable self-simulation might eventually reduce dependence on external test-case generators or interpreters during inference.
The approach could be tested on longer or more stateful programs where execution traces become harder to predict accurately.

Load-bearing premise

That models can learn sufficiently accurate step-by-step execution simulation from the provided traces to produce reliable self-verification and self-fixing feedback on unseen competitive programming problems.

What would settle it

A controlled test in which models trained with the method show no performance gain over baselines on a fresh set of competitive programming problems when the training traces are replaced with deliberately noisy or incorrect execution information.

Figures

Figures reproduced from arXiv: 2604.03253 by Felix Kreuk, Gal Cohen, Gallil Maimon, Michael Hassid, Ori Yoran, Pierre Chambon, Yossi Adi.

**Figure 1.** Figure 1: A conceptual outline of how one can use self-execution simulation of a generated code solution (or solutions) on public or generated test cases to improve coding performance. The simulation can be used as feedback to select the best solution from a few candidates (best@k) or to iteratively fix the code as needed (self-RLEF). See Section 3 for details. absolute points on competitive programming tasks. In th… view at source ↗

**Figure 2.** Figure 2: The two parts of our training pipeline. 1) Supervised fine tuning on natural language execution traces (NLEX), 2) Multi-task reinforcement learning on output prediction and competitive programming (optionally with multi-turn feedback and fixing). wise, allowing 1e − 5 tolerance in float comparisons. The intended downstream use of the output prediction ability is simulating the execution of model generated… view at source ↗

**Figure 3.** Figure 3: CruxEval-O performance compared to model active parameters. Arrows demonstrate the benefit from training on NLEX data. We also compare to open models. LCB-IO. We curate a subset from LiveCodeBench-v6 (Jain et al., 2024) containing only problems evaluated via stdio tests, which we refer to as LCB-IO. This restriction simplifies output prediction, as the task reduces to determining the content written to st… view at source ↗

**Figure 4.** Figure 4: Best@k performance of self-verification with self-simulation. Solutions and output predictions are produced by the same model - based on Qwen2.5-7B or CWM, trained for both solving and output prediction. Even though the tests used for filtering are in the solve prompt, there is still room for notable gains from simulating them [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Comparing best@k when ranking Qwen3-32B solutions, using CWM post-trained only for output prediction as a verifier [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Comparing best@k when ranking solutions generated by CWM post-trained jointly for solving and output prediction, using the same model as a verifier. The model here was trained and evaluated without the public tests as part of the description [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗

**Figure 7.** Figure 7: Comparing best@k when ranking Qwen3-4B solutions, using CWM post-trained only for output prediction as a verifier. We also provide results of using a dedicated verifier based on a smaller model (Qwen2.5-7B), on solutions generated by a model starting from the same base model. Results provided in [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 8.** Figure 8: Comparing best@k when ranking solutions by CWM post-trained only for competitive programming solving (denoted SOLVERL in [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

**Figure 9.** Figure 9: Comparing best@k when ranking solutions by Qwen-7B post-trained for competitive programming solving, using Qwen-7B post-trained only for output prediction as a verifier. This mirrors the results for Qwen in [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗

read the original abstract

A promising research direction in enabling LLMs to generate consistently correct code involves addressing their inability to properly estimate program execution, particularly for code they generate. In this work, we demonstrate that Code LLMs can be trained to simulate program execution in a step-by-step manner and that this capability can be leveraged to improve competitive programming performance. Our approach combines supervised fine-tuning on natural language execution traces, textual explanations grounded in true execution, with reinforcement learning using verifiable rewards. We introduce two complementary objectives: output prediction given code and inputs, and solving competitive programming tasks with either ground-truth or self-predicted execution feedback. These objectives enable models to perform self-verification over multiple candidate solutions, and iterative self-fixing by simulating test execution. Across multiple competitive programming benchmarks, our method yields consistent improvements over standard reasoning approaches. We further present ablations and analysis to elucidate the role of execution simulation and its limitations.

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

The paper trains code LLMs on grounded execution traces via SFT then RL with mixed ground-truth and self-predicted feedback, producing measurable gains on competitive programming tasks.

read the letter

The main takeaway is that teaching models to simulate execution in natural language, then letting them use their own simulations for verification and iterative fixing during RL, improves results over plain reasoning baselines on comp prog benchmarks. The pipeline is straightforward: SFT on real traces to build the simulation skill, followed by RL objectives that reward both correct output prediction and task solving with either oracle or self-generated execution signals. This setup lets the model check multiple candidates and patch errors by running mental test executions. It is a clean way to make the model treat execution as something it can reason about explicitly rather than guess at. The ablations they run help isolate how much the simulation component contributes versus standard fine-tuning. The work stays grounded because rewards come from actual program runs, not from the model's internal consistency alone. One soft spot is the assumption that simulation accuracy transfers to the model's own generated code on unseen problems with complex loops, recursion, or data structures. If error rates rise there, the self-fixing loop could amplify mistakes instead of correcting them. The abstract and method description do not give explicit simulation error bounds or failure breakdowns on hard control flow, so that link needs the full numbers to judge how robust it really is. This paper is for groups already working on LLM code generation for verifiable settings like competitive programming or unit-tested functions. Readers who want a concrete training recipe with execution feedback will get practical value from the details. It is solid enough on method and evidence to deserve peer review rather than a desk reject.

Referee Report

2 major / 0 minor

Summary. The manuscript claims that Code LLMs can be trained to simulate program execution step-by-step in natural language, and that this capability improves competitive programming performance. The method combines supervised fine-tuning on natural-language execution traces grounded in true execution with reinforcement learning using verifiable rewards. It introduces two objectives—output prediction given code and inputs, and task solving with either ground-truth or self-predicted execution feedback—enabling self-verification over candidate solutions and iterative self-fixing. The paper reports consistent improvements over standard reasoning approaches across benchmarks, supported by ablations and analysis.

Significance. If the empirical results hold with proper quantitative support, the work would meaningfully advance LLM code generation by directly targeting execution estimation, a core limitation. The use of grounded traces for SFT and verifiable rewards for RL offers a concrete mechanism for self-verification that could improve reliability on tasks requiring precise control-flow reasoning.

major comments (2)

Abstract: The central claim of 'consistent improvements over standard reasoning approaches' is stated without any quantitative results, baseline details, ablation numbers, or error analysis. This absence is load-bearing because the soundness of the self-execution simulation approach cannot be evaluated without these data.
Method description: The approach rests on the assumption that models learn sufficiently accurate step-by-step execution simulation from the provided traces to enable reliable self-verification and self-fixing on unseen competitive programming problems. No quantitative bound on simulation error rates, ablation isolating self-predicted versus ground-truth feedback, or analysis of compounding errors in loops/recursion is supplied, leaving the least-secured link of the central claim untested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to provide the requested quantitative details and analyses.

read point-by-point responses

Referee: Abstract: The central claim of 'consistent improvements over standard reasoning approaches' is stated without any quantitative results, baseline details, ablation numbers, or error analysis. This absence is load-bearing because the soundness of the self-execution simulation approach cannot be evaluated without these data.

Authors: We agree that the abstract would be strengthened by including quantitative support. In the revised version, we will add specific performance gains across benchmarks, baseline comparisons, key ablation results, and a brief reference to error analysis to make the central claims directly evaluable. revision: yes
Referee: Method description: The approach rests on the assumption that models learn sufficiently accurate step-by-step execution simulation from the provided traces to enable reliable self-verification and self-fixing on unseen competitive programming problems. No quantitative bound on simulation error rates, ablation isolating self-predicted versus ground-truth feedback, or analysis of compounding errors in loops/recursion is supplied, leaving the least-secured link of the central claim untested.

Authors: The manuscript already includes ablations and analysis on the role of execution simulation and its limitations. However, we acknowledge the absence of explicit quantitative bounds on simulation error rates, a dedicated ablation isolating self-predicted versus ground-truth feedback, and targeted analysis of compounding errors in loops and recursion. We will expand the method and results sections in revision to include these quantifications and analyses to better substantiate the reliability of self-verification. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents an empirical training methodology that combines supervised fine-tuning on natural-language execution traces with reinforcement learning using externally verifiable rewards. No mathematical equations, derivations, or self-referential fitting steps appear in the abstract or method description. The central claims rest on benchmark improvements from SFT+RL objectives rather than any quantity being defined in terms of itself or renamed as a prediction. Verifiable execution rewards are external to the model, and the approach does not rely on load-bearing self-citations or uniqueness theorems. The derivation chain is therefore self-contained through standard empirical validation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven premise that LLMs can acquire accurate execution simulation from textual traces and that this simulation transfers to useful self-correction on competitive programming tasks; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption LLMs can be trained to simulate program execution in a step-by-step manner from natural language traces
Invoked as the foundation for both supervised fine-tuning and the RL objectives.

pith-pipeline@v0.9.0 · 5462 in / 1161 out tokens · 44197 ms · 2026-05-15T13:34:18.553030+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 2 internal anchors

[1]

Teaching Large Language Models to Self-Debug

URL https://openreview.net/forum? id=YfZ4ZPt8zd. Chen, X., Lin, M., Sch ¨arli, N., and Zhou, D. Teaching large language models to self-debug.arXiv preprint arXiv:2304.05128, 2023c. Cobbe, K., Kosaraju, V ., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J. Training verifiers to solve...

work page internal anchor Pith review Pith/arXiv arXiv 2021
[2]

Gehring, J., Zheng, K., Copet, J., Mella, V ., Cohen, T., and Synnaeve, G

URL https://proceedings.mlr.press/ v202/gao23f.html. Gehring, J., Zheng, K., Copet, J., Mella, V ., Cohen, T., and Synnaeve, G. RLEF: Grounding code LLMs in ex- ecution feedback with reinforcement learning. InForty- second International Conference on Machine Learning, 9 Self-Execution Simulation Improves Coding Models

work page
[3]

The Llama 3 Herd of Models

URL https://openreview.net/forum? id=PzSG5nKe1q. Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024. Gu, A., Li, W.-D., Jain, N., Olausson, T., Lee, C., Sen, K., and Solar-Lezama, A. The counterfeit conundru...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/forge66646.2025.00008 2024
[4]

A variable ‘dp‘ is initialized with the same length as the array, filled with 0s

The input array is ‘[1, 0, 0, 0, 0, 0]‘. A variable ‘dp‘ is initialized with the same length as the array, filled with 0s

work page
[5]

The first element of ‘dp‘ is set to the first element of the input array, which is ‘1‘

work page
[6]

A variable ‘result‘ is also initialized with the first element of the array, which is ‘1‘, representing the current maximum subsequence sum

work page
[7]

The code enters a loop to process each element in the array starting from the second position

work page
[8]

So, ‘dp[1] = 1‘

At the first iteration (‘i = 1‘), the value of ‘dp[i]‘ is computed as the maximum of the current element ‘0‘ and the sum of the previous ‘dp‘ value ‘1‘ and the current element. So, ‘dp[1] = 1‘. The ‘result‘ is updated to the maximum of itself and ‘dp [1]‘, remaining ‘1‘

work page
[9]

So, ‘dp[2] = 1‘

At the second iteration (‘i = 2‘), the value of ‘dp[2]‘ is computed similarly as the maximum of ‘0‘ and ‘1 (dp[i-1]) + 0‘. So, ‘dp[2] = 1‘. The ‘result‘ still stays at ‘1‘

work page
[10]

- For each, the value of ‘dp[i]‘ at ‘0‘ will be compared with the sum of the previous ‘dp[i-1]‘ value (which was updated in the prior step, incrementally adding

This process is repeated for the remaining indices (3, 4, and 5). - For each, the value of ‘dp[i]‘ at ‘0‘ will be compared with the sum of the previous ‘dp[i-1]‘ value (which was updated in the prior step, incrementally adding

work page
[11]

Thus, ‘dp[i]‘ will become ‘1‘ for each index ‘i >= 2‘

and ‘0‘. Thus, ‘dp[i]‘ will become ‘1‘ for each index ‘i >= 2‘. - The ‘result‘ continues to remain at ‘1‘ since all updated ‘dp[i]‘ values are equal to the current maximum

work page
[12]

[/THOUGHT] [ANSWER] assert maxSubArrayDP([1, 0, 0, 0, 0, 0]) == 1 [/ANSWER] 18 Self-Execution Simulation Improves Coding Models Ex

After all iterations, the loop finishes, and the final value of ‘result‘ is returned , which is the maximum sum of any contiguous subarray of the input array. [/THOUGHT] [ANSWER] assert maxSubArrayDP([1, 0, 0, 0, 0, 0]) == 1 [/ANSWER] 18 Self-Execution Simulation Improves Coding Models Ex. 2 - Code + Input [PYTHON] deftranslate(value, to_min, to_max, from...

work page
[13]

At the start of the function, the local variables ‘value‘, ‘to_min‘, ‘to_max‘, ‘ from_min‘, and ‘from_max‘ are assigned the values 11, 0, 10, 0, and 20 respectively

work page
[14]

On the sixth line of the function, the variable ‘to_span‘ is calculated by subtracting ‘to_min‘ from ‘to_max‘, resulting in ‘10 - 0 = 10‘

work page
[15]

On the seventh line, the variable ‘from_span‘ is calculated by subtracting ‘from_min ‘ from ‘from_max‘, resulting in ‘20 - 0 = 20‘

work page
[16]

On the ninth line, the variable ‘spans_decimal‘ is calculated by dividing ‘to_span‘ by ‘from_span‘, resulting in ‘10 / 20 = 0.5‘

work page
[17]

- Then, this result is multiplied by ‘spans_decimal‘, resulting in ‘11 * 0.5 = 5.5‘

On the twelfth line, a new variable ‘scaled_value‘ is calculated in several steps: - First, the difference between ‘value‘ and ‘to_min‘ is calculated, resulting in ‘11 - 0 = 11‘. - Then, this result is multiplied by ‘spans_decimal‘, resulting in ‘11 * 0.5 = 5.5‘. - The result is rounded to one decimal place, which may not be consistent with the code: ‘rou...

work page
[18]

[/THOUGHT] [ANSWER] assert translate(11, 0, 10, 0, 20) == 6 [/ANSWER] Ex

On the fourteenth line, the ‘scaled_value‘ (6) is returned. [/THOUGHT] [ANSWER] assert translate(11, 0, 10, 0, 20) == 6 [/ANSWER] Ex. 3 - Code + Input [PYTHON] defadditionLossFunc(x, inc): y = [] foriinx: y.append(inc*i*100) returny assertadditionLossFunc([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 1.25) == ?? [/PYTHON] Ex. 3 - Natural Language Execution Trace [THOU...

work page

[1] [1]

Teaching Large Language Models to Self-Debug

URL https://openreview.net/forum? id=YfZ4ZPt8zd. Chen, X., Lin, M., Sch ¨arli, N., and Zhou, D. Teaching large language models to self-debug.arXiv preprint arXiv:2304.05128, 2023c. Cobbe, K., Kosaraju, V ., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J. Training verifiers to solve...

work page internal anchor Pith review Pith/arXiv arXiv 2021

[2] [2]

Gehring, J., Zheng, K., Copet, J., Mella, V ., Cohen, T., and Synnaeve, G

URL https://proceedings.mlr.press/ v202/gao23f.html. Gehring, J., Zheng, K., Copet, J., Mella, V ., Cohen, T., and Synnaeve, G. RLEF: Grounding code LLMs in ex- ecution feedback with reinforcement learning. InForty- second International Conference on Machine Learning, 9 Self-Execution Simulation Improves Coding Models

work page

[3] [3]

The Llama 3 Herd of Models

URL https://openreview.net/forum? id=PzSG5nKe1q. Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024. Gu, A., Li, W.-D., Jain, N., Olausson, T., Lee, C., Sen, K., and Solar-Lezama, A. The counterfeit conundru...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/forge66646.2025.00008 2024

[4] [4]

A variable ‘dp‘ is initialized with the same length as the array, filled with 0s

The input array is ‘[1, 0, 0, 0, 0, 0]‘. A variable ‘dp‘ is initialized with the same length as the array, filled with 0s

work page

[5] [5]

The first element of ‘dp‘ is set to the first element of the input array, which is ‘1‘

work page

[6] [6]

A variable ‘result‘ is also initialized with the first element of the array, which is ‘1‘, representing the current maximum subsequence sum

work page

[7] [7]

The code enters a loop to process each element in the array starting from the second position

work page

[8] [8]

So, ‘dp[1] = 1‘

At the first iteration (‘i = 1‘), the value of ‘dp[i]‘ is computed as the maximum of the current element ‘0‘ and the sum of the previous ‘dp‘ value ‘1‘ and the current element. So, ‘dp[1] = 1‘. The ‘result‘ is updated to the maximum of itself and ‘dp [1]‘, remaining ‘1‘

work page

[9] [9]

So, ‘dp[2] = 1‘

At the second iteration (‘i = 2‘), the value of ‘dp[2]‘ is computed similarly as the maximum of ‘0‘ and ‘1 (dp[i-1]) + 0‘. So, ‘dp[2] = 1‘. The ‘result‘ still stays at ‘1‘

work page

[10] [10]

- For each, the value of ‘dp[i]‘ at ‘0‘ will be compared with the sum of the previous ‘dp[i-1]‘ value (which was updated in the prior step, incrementally adding

This process is repeated for the remaining indices (3, 4, and 5). - For each, the value of ‘dp[i]‘ at ‘0‘ will be compared with the sum of the previous ‘dp[i-1]‘ value (which was updated in the prior step, incrementally adding

work page

[11] [11]

Thus, ‘dp[i]‘ will become ‘1‘ for each index ‘i >= 2‘

and ‘0‘. Thus, ‘dp[i]‘ will become ‘1‘ for each index ‘i >= 2‘. - The ‘result‘ continues to remain at ‘1‘ since all updated ‘dp[i]‘ values are equal to the current maximum

work page

[12] [12]

[/THOUGHT] [ANSWER] assert maxSubArrayDP([1, 0, 0, 0, 0, 0]) == 1 [/ANSWER] 18 Self-Execution Simulation Improves Coding Models Ex

After all iterations, the loop finishes, and the final value of ‘result‘ is returned , which is the maximum sum of any contiguous subarray of the input array. [/THOUGHT] [ANSWER] assert maxSubArrayDP([1, 0, 0, 0, 0, 0]) == 1 [/ANSWER] 18 Self-Execution Simulation Improves Coding Models Ex. 2 - Code + Input [PYTHON] deftranslate(value, to_min, to_max, from...

work page

[13] [13]

At the start of the function, the local variables ‘value‘, ‘to_min‘, ‘to_max‘, ‘ from_min‘, and ‘from_max‘ are assigned the values 11, 0, 10, 0, and 20 respectively

work page

[14] [14]

On the sixth line of the function, the variable ‘to_span‘ is calculated by subtracting ‘to_min‘ from ‘to_max‘, resulting in ‘10 - 0 = 10‘

work page

[15] [15]

On the seventh line, the variable ‘from_span‘ is calculated by subtracting ‘from_min ‘ from ‘from_max‘, resulting in ‘20 - 0 = 20‘

work page

[16] [16]

On the ninth line, the variable ‘spans_decimal‘ is calculated by dividing ‘to_span‘ by ‘from_span‘, resulting in ‘10 / 20 = 0.5‘

work page

[17] [17]

- Then, this result is multiplied by ‘spans_decimal‘, resulting in ‘11 * 0.5 = 5.5‘

On the twelfth line, a new variable ‘scaled_value‘ is calculated in several steps: - First, the difference between ‘value‘ and ‘to_min‘ is calculated, resulting in ‘11 - 0 = 11‘. - Then, this result is multiplied by ‘spans_decimal‘, resulting in ‘11 * 0.5 = 5.5‘. - The result is rounded to one decimal place, which may not be consistent with the code: ‘rou...

work page

[18] [18]

[/THOUGHT] [ANSWER] assert translate(11, 0, 10, 0, 20) == 6 [/ANSWER] Ex

On the fourteenth line, the ‘scaled_value‘ (6) is returned. [/THOUGHT] [ANSWER] assert translate(11, 0, 10, 0, 20) == 6 [/ANSWER] Ex. 3 - Code + Input [PYTHON] defadditionLossFunc(x, inc): y = [] foriinx: y.append(inc*i*100) returny assertadditionLossFunc([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 1.25) == ?? [/PYTHON] Ex. 3 - Natural Language Execution Trace [THOU...

work page