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TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs

Zihan Lin, Xiaohan Wang, Jie Cao, Jiajun Chai, Wei Lin, Guojun Yin, Ran He

arXiv:2609.33589v1cs.LGcs.CL

TL;DR

RLVR exploration is constrained by strategies that spend more rollout samples or do not quantify exploration’s benefit. TGRL estimates gain from low- and high-temperature reward contrasts and allocates it to temperature-sensitive tokens; it reports improvements across reasoning, coding, and agent tasks, with a stated scope limited to verifiable-reward settings.

  • Problem

    RLVR needs diverse, high-reward trajectories within constrained rollout budgets, while existing diversity strategies either expand sampling or leave exploration benefit unquantified.

    Source: §1 · pp. 1–2

  • Method

    TGRL compares low- and high-temperature rollout rewards per prompt, then uses token-level JS divergence to allocate the resulting exploration signal as credit.

    Source: §1 · p. 2, §3.1 · p. 3, §3.2 · p. 6

  • Results

    Across mathematical reasoning, code generation, and long-horizon agent tasks, TGRL improves over strong RLVR baselines and reaches 70.2% average math accuracy on Qwen3-32B.

    Source: §5 · p. 10

  • Takeaways & Limitations

    Ablations and training dynamics indicate that mixed-temperature grouping and JS-based credit allocation provide complementary benefits.

    Source: §5 · p. 10

    TGRL is designed for verifiable-reward tasks and is not claimed to directly solve open-ended dialogue, subjective preference optimization, safety-critical decisions, or tasks with noisy, delayed, or difficult-to-verify rewards.

    Source: L Limitations and Broader Impact · p. 41

Abstract

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Efficient exploration often remains a central bottleneck in reinforcement learning with verifiable rewards (RLVR). Although temperature control and test-time scaling strategies can increase rollout diversity of large language models (LLMs), they either expand the sample budget at rollout time or leave the benefit of exploration unquantified. To this end, we propose Temperature-Grouped Reinforcement Learning (TGRL), which turns temperature-induced diversity into an explicit training signal. For each prompt, TGRL partitions its rollout group into low- and high-temperature subsets, estimates exploration gain through their reward contrast, and allocates this group-level signal as token-level credit using Jensen--Shannon (JS) divergence between the corresponding temperature-scaled next-token distributions induced by the same logits. Notably, TGRL reaches equivalent accuracy up to 36% faster than strong RLVR baselines without expanding the rollout budget. Across 11 benchmarks from diverse domains, TGRL broadly improves over strong RLVR baselines: it improves the six-benchmark math average by 1.6% at 32B, raises CodeForces rating by 196.7 points and LiveCodeBench Pass@16 by 4.4%, and improves ALFWorld/WebShop success rates by 6.3%/4.9%. Comprehensive ablations and wall-clock analysis confirm the efficacy of all proposed components. Code is available at https://github.com/1229095296/TGRL/tree/main.

1 Introduction

Efficient exploration is central to RLVR, but existing approaches either increase rollout sampling or leave exploration’s benefit unquantified. TGRL uses temperature-induced reward contrasts and token-level JS divergence to turn exploration into a training signal, reporting gains across reasoning, coding, and agent benchmarks.

1 Introduction

  • RLVR exploration requires diverse, high-reward trajectories within a constrained rollout budget, because trajectory diversity affects optimization and attainable reasoning gains.Without robust exploration, RLVR may prematurely converge to a narrow decoding mode.Source: §1 · p. 1
  • Existing diversity strategies either expand the rollout sample budget or leave the benefit of exploration unquantified.The introduction argues for connecting active sampling to an explicit exploration-gain estimate in the policy-gradient update.Source: §1 · p. 2
  • TGRL estimates prompt-level exploration gain from the mean-reward gap between low- and high-temperature rollout groups, then allocates it to temperature-sensitive tokens using JS divergence.The resulting token-level credit is used in policy-gradient updates.Source: §1 · p. 2, Figure 1 · p. 2
  • Across 11 benchmarks, TGRL improves the six-benchmark math average by 1.6% at 32B, CodeForces rating by 196.7 points, and ALFWorld/WebShop success by 6.3%/4.9%.It also raises LiveCodeBench Pass@16 by 4.4%.Source: §1 · p. 2

2 Related Work

Prior RLVR work has strengthened policy-gradient optimization, while exploration approaches use sampling, decoding changes, or training-time objectives. The paper identifies a gap in reward-grounded, prompt-level estimation and token-level allocation of exploration gain.

RLVR optimization algorithms

  • RLVR optimization methods refine group-based policy-gradient training, but commonly assume rollout groups already contain sufficiently diverse trajectories.This leaves active estimation and exploitation of exploration gain within a fixed budget unaddressed in those approaches.Source: §2 · pp. 2–3

Exploration strategies in RL

  • Exploration methods include test-time sampling, search, or verification, plus training-time filtering, grouping, entropy/search objectives, and temperature strategies.Test-time scaling typically increases the number of sampled trajectories or rollouts, making exploration costly.Source: §2 · p. 3
  • The paper distinguishes TGRL by estimating prompt-level exploration gain from mixed-temperature reward gaps and allocating it as token-level credit through JS divergence.Prior approaches are characterized as treating exploration mainly as a global regularizer, auxiliary objective, or decoding policy.Source: §2 · p. 3

3 Method

TGRL separates two roles: mixed-temperature grouping estimates whether broader exploration improves reward, and token-level JS divergence allocates that signal to temperature-sensitive positions. Its algorithm applies the resulting credit to high-temperature rollouts in a clipped policy objective.

3 Method

  • TGRL’s method combines theoretical analysis of exploration-gain estimation and token-level credit allocation with their joint algorithmic instantiation.The analysis treats the reward gap as the exploration signal and JS divergence as the token-level allocation mechanism.Source: §3 · p. 3, §3.1 · p. 3

3.1 Theoretical Analysis

  • For fixed logits, token-level JS divergence measures how strongly a next-token distribution responds to switching temperature.The analysis characterizes it as local sensitivity to the temperature intervention.Source: §3.1 · p. 3

3.1.1 Exploration Gain Estimation

  • The low-/high-temperature reward gap is an unbiased estimate of prompt-level exploration gain under the stated sampling setup.The passage contrasts this with uniform-temperature group normalization, which cannot directly recover the within-prompt temperature contrast.Source: §3.1.1 · pp. 3–4

Consider the mixed-group advantage

  • Mixed-group normalization shifts every high-temperature advantage by an additive term proportional to the estimated exploration gain.The subgroup-mean identity is reported as following from averaging the mixed-group advantages.Source: Consider the mixed-group advantage · p. 4

3.1.2 Token-Level Credit Allocation

  • Larger token-level JS identifies positions more sensitive to temperature; monotone JS weighting allocates more credit to positions with larger certified sensitivity.A local-cooling probe reports JS-targeted spans had the highest flip rate (12.1%) and score drop (0.241) at matched position ratios.Source: §3.1.2 · pp. 4–5

3.2 Algorithm

  • The algorithm uses a clipped importance-ratio objective and optimizes high-temperature rollouts, assigning token weights from JS sensitivity after log compression and trajectory-wise normalization.Low-temperature rollouts contribute to group statistics but receive zero token-advantage gradient.Source: §3.2 · pp. 5–6

4 Experiments

Experiments evaluate TGRL across mathematical reasoning, code generation, and long-horizon agent tasks, alongside training-dynamics analyses and component ablations. Results favor TGRL across domains and support complementary roles for mixed-temperature grouping and JS-based credit allocation.

4.1 Experimental Setup

  • The suite spans math, code, and agent tasks; training uses four responses per prompt, with one T0=0.3 reference rollout and three exploratory rollouts.Experiments use Qwen3-4B/14B/32B for mathematics, Qwen3-4B for code, and Qwen2.5-7B-Instruct for agent tasks.Source: §4.1 · pp. 6–7

4.2 Main Results

  • At Qwen3-32B, TGRL achieves the best six-benchmark math average, 70.2%, outperforming the strongest baseline by 1.6%; separately, it raises CodeForces rating from 1377.6 to 1574.3 (+196.7) and reaches 86.7% ALFWorld success (+6.3%).Source: §4.2 · pp. 7–8
  • TGRL improves LiveCodeBench Pass@16 by 4.4% over the strongest prior result and reaches 74.2% WebShop success (+4.9%).On near-saturated HumanEval+, TGRL remains competitive with top baselines.Source: §4.2 · pp. 7–8

4.3 Training Dynamics Analysis

  • On AIME24/AIME25, higher-temperature GRPO improves from 54.8%/37.5% to 60.0%/44.2%, but remains below TGRL at 61.9%/49.6%.The authors interpret the low-/high-temperature reward gap as an empirical estimate of prompt-level exploration gain.Source: §4.3 · p. 8
  • TGRL has the largest gradient norm (0.331) and policy KL (0.160) with PPO clip fraction below 0.11%, while averaging 2476 response tokens versus 3915 for GRPO @ T0.TGRL and uniform-token-credit TGRL have nearly identical late-stage rewards (0.394 versus 0.388), but differ in downstream accuracy, response length, and truncation.Source: §4.3 · pp. 8–9

4.4 Ablation Studies

  • Mixed-temperature grouping raises average accuracy from 67.0% to 68.2% at 14B and from 66.2% to 67.8% at 32B; adding JS credit reaches 69.4% and 70.2%.JS credit adds +2.5% on AIME25 at 14B and +3.5% at 32B.Source: §4.4 · p. 9
  • T1=0.8 degrades performance, while T1 ∈[1.0, 1.6] remains competitive; T1=1.2 gives the best reported efficiency–stability trade-off.T1=1.2 is adopted as the default for the main experiments.Source: §4.4 · p. 9
  • Against DAPO, TGRL reaches an equivalent accuracy target 36% faster (6.4 h vs. 10.0 h); within 12 hours, its AIME-Combined score is 1.0833 versus GRPO’s 1.0354.The matched-budget comparison reports a +0.048 absolute difference against GRPO.Source: §4.4 · p. 9

5 Conclusion

TGRL treats sampling temperature as a source of exploration signal, using low-temperature rollouts as a reference for high-temperature exploration. The supplementary theory explains how reward contrast estimates prompt-level exploration gain and how temperature-sensitive token positions can receive credit.

5 Conclusion

  • TGRL uses low-temperature reference and high-temperature exploration groups to estimate exploration gain from their reward gap and allocate token-level credit through JS divergence.The conclusion frames temperature as a way to construct an RLVR exploration signal, rather than merely a decoding parameter.Source: §5 · p. 9

A.1 Why Uniform-temperature Group Normalization Cannot Recover Exploration Gain

  • Uniform-temperature group normalization can rank samples within a group but cannot measure whether higher-temperature sampling helps on the same prompt.A single-temperature group contains no within-prompt temperature contrast; TGRL creates one by splitting rollouts between T0 and T1.Source: §A.1 · p. 15

A.2 Proof of Lemma 1 (Exploration Gain under Mixed-Temperature Grouping)

  • The low-temperature group supplies the within-prompt reference for estimating exploration gain, which enters the mixed-group advantage used for learning.The low-/high-temperature reward contrast estimates the difference in mean reward for a fixed prompt.Source: §A.2 · pp. 15–16

A.3 Proof of Proposition 1 (Mixed-group Advantage Decomposition)

  • Mixed-group advantages combine within-subgroup reward deviations with a cross-group term whose sign follows the prompt-level exploration gain.A positive gain shifts high-temperature samples upward and low-temperature samples downward; a negative gain reverses those shifts.Source: §A.3 · pp. 16–17

A.5 Proof of Lemma 2 (Global temperature-response control of token-level JS)

  • The supplementary theory characterizes token-level JS divergence as a local distributional response to temperature perturbation, grounding credit allocation to temperature-sensitive positions.The proof bounds JS using one eighth of the Jeffreys divergence.Source: §A.5 · p. 18

A.6 Local-cooling Probe for JS-based Credit Allocation

The local-cooling probe tests whether JS-weighted token selection identifies spans whose cooling harms successful trajectories. The accompanying theory and algorithm describe how temperature sensitivity shapes token credit and why the low-temperature group informs, but does not directly receive, actor updates.

A.6 Local-cooling Probe for JS-based Credit Allocation

  • 12.1% flip rate and 0.241 mean score drop for the JS selector exceed MATCHED-RANDOM (8.7%, 0.173), ENTROPY (7.6%, 0.152), and LOW-MARGIN (11.6%, 0.232).The probe cools fixed-budget contiguous spans in successful Qwen3-32B exploration trajectories; the table caption defines higher flip rate and score drop as greater temperature sensitivity.Source: §A.6 · p. 19, Table 6 · p. 20

A.8 Co-monotonicity and Chebyshev’s Order Inequality

  • JS-based temperature sensitivity captures information about cooling-induced verifier damage that entropy and top-two-logit margin do not capture.The probe reports JS > LOW-MARGIN > MATCHED-RANDOM > ENTROPY for both damage metrics.Source: §A.6 · p. 20

A.7 Proof of Proposition 2 (Credit Allocation toward Temperature-Sensitive Positions)

  • The practical token-weight function preserves each trajectory’s average rollout-level advantage while redistributing it across token positions.The proof connects this weighting profile to the proposition on credit allocation toward temperature-sensitive positions.Source: §A.7 · p. 21

A.9 Attribution Control for Excluding the Low-temperature Group from Direct Actor Updates

  • Low-temperature rollouts contribute to mixed-group statistics and exploration-gain estimation but receive no direct actor-gradient attribution.The zero-gradient design keeps the conservative reference from becoming a competing actor-update target; the default split uses one reference rollout and three exploratory update samples.Source: §A.9 · pp. 22, 24

B Algorithm Design

  • Algorithm 1 proceeds through mixed-temperature rollouts and grouped advantages, JS-based token credit allocation on high-temperature rollouts, and clipped-surrogate assembly.Low-temperature tokens are assigned zero token-level advantage.Source: B Algorithm Design · p. 25

C Time Complexity Analysis

Under a fixed rollout budget, TGRL has the same leading asymptotic order as standard group-based RLVR, with token-wise JS processing as lower-order overhead. This comparison assumes logits are reused between the JS scores and clipped-ratio term.

C Time Complexity Analysis

  • Under fixed group sizes and optimizer epochs, TGRL has the same leading asymptotic order as standard group-based RLVR, with additional lower-order token-wise JS post-processing.The comparison assumes reuse of current-policy logits computed on high-temperature trajectories for both the JS scores and clipped-ratio term.Source: C Time Complexity Analysis · pp. 26–27
  • Reusing logits lets TGRL replace low-temperature-trajectory model-update cost with JS post-processing on high-temperature trajectories.The extra JS computation is O(Ni|V|) once logits are available, versus a full Transformer forward-backward pass for model updates.Source: C Time Complexity Analysis · p. 27
  • Computing logits in a separate forward pass adds a high-temperature-group forward term and increases the constant factor without changing asymptotic order.The stated complexity bound assumes logits are shared between the JS and clipped-ratio calculations.Source: C Time Complexity Analysis · p. 27

D Wall-Clock Training Efficiency

The wall-clock comparisons test whether TGRL’s mixed-temperature rollouts and token-wise JS computation reduce its practical training efficiency. Under matched budgets, TGRL makes stronger validation progress than GRPO and DAPO, and reaches a DAPO-level target sooner.

Protocols

  • The comparisons use two clocks: the GRPO comparison starts after TGRL’s 20-step warmup, while the DAPO comparisons start at step 0.The post-warmup comparison isolates the training regimes; the from-start time-to-target protocol fixes an accuracy threshold and measures elapsed time.Source: D Wall-Clock Training Efficiency · p. 28

Analysis

  • Within a shared 12-hour post-warmup budget, TGRL’s best AIME-COMBINED score is 1.0833 versus GRPO’s 1.0354, a +0.048 absolute improvement.TGRL’s mixed-temperature construction and per-token JS computation do not produce a visible wall-clock penalty against single-temperature baselines under the fixed rollout budget.Source: D Wall-Clock Training Efficiency · pp. 28–29, Table 7 · p. 28
  • From step 0, TGRL attains a 12-hour best AIME-COMBINED score of 1.0604 versus DAPO’s 0.9521, a +0.108 absolute gap.This protocol includes TGRL’s first 20 single-temperature warmup steps.Source: D Wall-Clock Training Efficiency · pp. 28–29, Table 7 · p. 28
  • 36% faster: TGRL reaches the 0.9045 AIME-COMBINED target in 6.39 hours, while DAPO takes 10.01 hours to reach its best score of 0.9521.The comparison uses a from-start time-to-target protocol.Source: Table 7 · p. 28, D Wall-Clock Training Efficiency · p. 29

E Exploration-Oriented Baselines

Entropy-regularized GRPO and temperature-adaptive sampling improve on plain GRPO, but their benchmark gains are uneven and their prompt-agnostic designs limit prompt-specific adaptation. TGRL’s within-prompt grouping outperforms these exploration-oriented baselines on the six-benchmark average, with supplementary results showing the ordering also holds at 4B.

Exploration-oriented baselines

  • Entropy regularization and TAMPO improve over plain GRPO but have uneven benchmark profiles; raising the entropy coefficient to λ=10−3 lowers the average to 66.7.The mild entropy bonus (λ=10−4) raises the average from 67.0 to 67.8, while TAMPO reaches 68.0.Source: E Exploration-Oriented Baselines · p. 29
  • Both alternatives are prompt-agnostic: entropy regularization applies uniformly, while TAMPO uses one temperature per prompt group at each training step.The same global exploration push can be neutral or harmful on prompts already well-supported by low-temperature decoding.Source: E Exploration-Oriented Baselines · pp. 29–30
  • 69.4 average accuracy: TGRL exceeds the stronger entropy-regularized run by 1.6, TAMPO by 1.4, and plain GRPO by 2.4.The largest margins over the best non-TGRL entry are Olympiad (+2.6) and AIME25 (+1.0); TGRL is competitive rather than dominant on AMC23 and Minerva.Source: E Exploration-Oriented Baselines · p. 30

Supplementary results on Qwen3-4B

F Sensitivity to the Fixed Exploration Temperature on 4B

The 4B sweep varies the exploration temperature while holding the low-temperature reference at 0.3, testing performance and the basis for the default setting. A sufficiently large temperature gap produces a broad, non-monotonic robust region, with T1 = 1.2 offering a balance of efficiency, benchmark performance, and stable optimization.

Temperature sweep

Per-benchmark analysis

  • T1 = 0.8 is consistently weak across AIME24, AIME25, and GPQA, whereas settings from T1 = 1.0 to 1.6 remain competitive for substantial portions of training.The benchmark pattern is robust but non-monotonic; T1 = 1.4 and 1.6 are also strong on selected benchmarks.Source: F Sensitivity to the Fixed Exploration… · pp. 31–32

Optimization diagnostics

  • T1 = 1.2 combines near-best reward, short responses, and uniformly low clip fraction with smoother update dynamics than the transient early clip-fraction spike at T1 = 1.6.Validation quality does not increase monotonically with late-stage reward or decreasing response length.Source: Figure 3 · p. 31, F Sensitivity to the Fixed Exploration… · p. 32
  • The sweep supports choosing the exploration temperature by jointly considering learning efficiency, cross-benchmark robustness, and optimization stability rather than relying on a single proxy.The figure summarizes benchmark validation alongside training reward, response length, and policy clip fraction.Source: Figure 3 · p. 31, F Sensitivity to the Fixed Exploration… · p. 32

G Multi-Seed Evaluation on Competition Benchmarks

Five-seed Avg@16 evaluations test whether the competition-benchmark results are sensitive to sampling variance. Cross-seed standard deviations are typically small, and TGRL’s mean accuracy exceeds GRPO’s across most benchmark-scale combinations, with the clearest separation on AIME.

Evaluation setup

Multi-seed results

  • TGRL leads GRPO by 3.0–6.9 points on AIME benchmarks across scales, with mean gaps exceeding the individual standard deviations.The cross-seed standard deviations are typically 1–4 percentage points, and rankings remain stable across seeds.Source: G Multi-Seed Evaluation on Competition… · p. 32, Table 10 · p. 32
  • The reported multi-seed results support the reproducibility of the main-table improvements against evaluation variance.The authors report TGRL’s mean accuracy as higher than GRPO’s across the majority of benchmark-scale combinations.Source: G Multi-Seed Evaluation on Competition… · p. 32

H Robustness under Larger Rollout Budget

With eight rollouts per prompt, TGRL retains a six-benchmark advantage over GRPO, with gains again concentrated on competition-style benchmarks.

H Robustness under Larger Rollout Budget

  • TGRL splits its eight rollouts into two low-temperature reference and six high-temperature exploration responses, while GRPO uses one temperature.The TGRL split preserves the 1:3 reference-to-exploration ratio used in the main experiments.Source: H Robustness under Larger Rollout Budget · p. 33
  • At n=8, TGRL’s six-benchmark Avg@16 is 69.1% vs. 67.9% for GRPO, a +1.2 percentage-point gain.The corresponding n=8 table reports Qwen3-14B results.Source: Table 11 · p. 33, H Robustness under Larger Rollout Budget · p. 33
  • Competition benchmarks again show the clearest gains: TGRL improves over GRPO by 5.2% on AIME24 and 2.7 points on AIME25.On near-saturated benchmarks, the methods remain within 0.6% of each other in either direction.Source: H Robustness under Larger Rollout Budget · p. 33
  • Across n=4 and n=8, TGRL’s advantage over GRPO is stable and does not depend on a particular rollout-budget allocation size.The reported improvement on challenging competition benchmarks is consistent across both settings.Source: H Robustness under Larger Rollout Budget · p. 33

I Sensitivity to the Low-/High-Temperature Split

Under a fixed four-rollout budget, the default split of one low-temperature reference and three high-temperature exploration rollouts gives the best overall average.

I Sensitivity to the Low-/High-Temperature Split

  • The default (1, 3) split outperforms (2, 2) by 2.3 points and (3, 1) by 1.3 points in overall average.Table 12 compares Avg@16 mathematical-reasoning results under the same four-rollout budget.Source: Table 12 · p. 34, I Sensitivity to the… · p. 34
  • Both alternative splits remain competitive, but one low-temperature rollout supplies a reference while three high-temperature rollouts provide exploration samples for estimation and updates.The authors describe this as a balance between a lightweight exploitation reference and a sufficiently large exploration branch.Source: I Sensitivity to the… · p. 34
  • A (2, 2) split reduces high-temperature update samples, while (3, 1) leaves only one exploratory trajectory and weakens broader cross-benchmark gains.The latter split performs well on AIME25 and AMC23 but reduces performance on MATH500, Minerva, and Olympiad.Source: I Sensitivity to the… · p. 34

J Synthetic Validation of Mixed-Temperature Grouping

This appendix examines mixed-temperature grouping in a controlled synthetic task and documents implementation, reproducibility, and scope. It also records checklist declarations on ethics, societal impact, assets, human-subject research, and LLM usage.

J Synthetic Validation of Mixed-Temperature Grouping

  • The synthetic diagnostic evaluates exploration-gain detection with AUROC and the mean advantage assigned to exploration samples, using four-seed confidence intervals.The figure caption specifies the metrics and 95% confidence intervals; the supplied passages do not provide plotted values or establish which comparison performs better.Source: Figure 4 · p. 35, J Synthetic Validation of… · p. 37
  • At warm start, correct-branch answer probability is 0.943 on both easy and hard prompts, while correct-branch probability on hard prompts is 0.044; positive exploration gain coincides with hard prompts across four seeds.Hardness comes from a branch-biased initialization, not missing answer supervision under either branch.Source: J Synthetic Validation of… · p. 36
  • The diagnostic measures scores at the common pre-RL warm-start checkpoint, with four rollouts per prompt: one low-temperature and three high-temperature samples, against a same-temperature control.The control uses only high-temperature samples and randomly splits them into pseudo-groups, removing the temperature gap while preserving the total sample budget.Source: J Synthetic Validation of… · p. 36

K Implementation and Reproducibility Details

  • Baseline comparisons match rollout budget, data, evaluator, checkpoint selection, and test-time decoding; TGRL receives no benchmark-specific retuning, stronger initialization, or extra rollouts.The stated differences are the algorithmic mechanisms under study: mixed-temperature grouping, exploration-gain estimation, and JS-based token credit allocation.Source: K Implementation and Reproducibility… · p. 39, Table 15 · p. 40

L Limitations and Broader Impact

  • TGRL targets reinforcement learning with verifiable rewards and is not claimed to directly solve open-ended dialogue, subjective preference optimization, safety-critical decisions, or tasks with hard-to-verify feedback.The paper identifies mathematical reasoning, code generation, and benchmarked agent tasks as settings suited to verifiable rewards.Source: L Limitations and Broader Impact · p. 41

NeurIPS Paper Checklist

  • The checklist states that the paper provides experimental details and open-access code/data or an anonymized supplementary package, and reports uncertainty measures such as multi-seed statistics and confidence intervals.The checklist says the reported uncertainty reflects sources of variability explained by the paper.Source: NeurIPS Paper Checklist · pp. 43–44
  • The authors state that the research conforms to the NeurIPS Code of Ethics and that the paper discusses societal impacts.The supplied passages do not specify those impacts.Source: NeurIPS Paper Checklist · pp. 45–46
  • The paper says it releases no high-risk model, scraped dataset, or other asset requiring special misuse safeguards.It also says existing assets are credited and their licenses or terms of use are stated where applicable.Source: NeurIPS Paper Checklist · pp. 46–47
  • New assets are documented with intended use, implementation details, and limitations, with documentation provided in supplementary material or the release package.Source: NeurIPS Paper Checklist · p. 47
  • The paper reports no crowdsourcing or human-subject research, so participant materials, compensation details, and IRB approval are not applicable.Source: NeurIPS Paper Checklist · pp. 47–48
  • LLMs were used only for grammar, spelling, and word-choice assistance.Source: NeurIPS Paper Checklist · p. 48

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