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Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective

Jaehyeon Kim, Suhwan Kim, Nakyung Lee, Yeongoon Kim, Jimin Seo, Giho Lee, Jungwoo Lee

arXiv:2609.04489v1cs.CLcs.AI

TL;DR

Pause-token gains have been explained through computation and expressivity, but their effects on fine-tuning dynamics are less studied. The paper tests two training-dynamics hypotheses and formalizes Masked Boundary Pause training. It reports reasoning improvements across model scales, preservation of general capability, and gains extending to GRPO.

  • Problem

    Prior explanations of pause-token gains focus on computation or effects at inserted positions, while their influence on pretrained distributions during fine-tuning remains less understood.

    Source: §1 · p. 1

  • Method

    The paper uses controlled pilots to motivate mode retention and non-myopic compression, then formalizes MBP with boundary-inserted pause tokens whose loss is masked.

    Source: §1 · pp. 1–2, §2.1 · p. 3

  • Results

    Across 1B–8B models, MBP improves math and code reasoning while preserving general capability, and gains extend to GRPO.

    Source: §1 · p. 2, §10 · p. 9

  • Takeaways & Limitations

    The authors frame pause tokens as a training-dynamics intervention on retention and adaptation, rather than only an inference-time computation device.

    Source: §1 · p. 1, §10 · pp. 9–10

    The study focuses on full fine-tuning and per-example correctness for retention; initial LoRA gains are more moderate, so parameter-efficient adaptation requires further study.

    Source: Limitations · p. 10

Abstract

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Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains these gains through computational expressivity. However, there is relatively little investigation into the training dynamics of pause tokens. We explore how pause tokens reshape the training dynamics of fine-tuning. Two controlled pilots expose distinct asymmetries. On a synthetic continual-learning task, masked pauses overwrite a previously-learned distribution roughly 4x less at matched final adaptation (H1, mode retention); on a synthetic math-reasoning probe, the boundary-adjacent token comes to encode substantially more downstream-step information (H2, non-myopic compression). We formalize a training rule consistent with both - Masked Boundary Pause (MBP), pause tokens placed at reasoning-step boundaries with their loss masked. Across 1B-8B Qwen and Llama models, MBP consistently improves reasoning, achieving gains of up to 6 points on math and 2.5 points on code, while preserving general language understanding abilities. We further demonstrate that this mode-preserving strategy extend gains to GRPO. These results recast pause tokens as a training-dynamics intervention on the retention-adaptation trade-off, rather than merely an inference-time computation device.

1 Introduction

Prior pause-token explanations focus on computation or effects at inserted positions, while their influence on fine-tuning dynamics remains less studied. The paper proposes masked boundary pauses and tests their effects on retention, reasoning, and scaled fine-tuning.

1 Introduction

  • Prior accounts attribute pause-token gains to computation, focal-loss amplification, or structural anchoring, but their effects on pretrained distributions during fine-tuning are less understood.The paper frames this gap as important for adaptation pipelines that warm-start from existing checkpoints.Source: §1 · p. 1
  • In a controlled continual-learning setting, loss-masked pauses reduce overwriting of a previously learned distribution by roughly 4× at matched final adaptation.The paper presents this as a retention–adaptation trade-off effect of training dynamics, including when pauses are absent at inference and are never supervised targets.Source: §1 · p. 1
  • Two controlled pilots motivate H1, mode retention, and H2, non-myopic compression.They examine retention on synthetic continual learning and information packing on an iGSM reasoning probe.Source: §1 · pp. 1–2
  • MBP inserts pause tokens at sentence or reasoning-step boundaries while masking their cross-entropy loss.The paper contrasts this with standard SFT, which applies next-token loss at every task-token position.Source: §1 · p. 2, Figure 1 · p. 2
  • Across 1B–8B models, MBP improves math and code reasoning while preserving general capability, and its gains extend to GRPO.The authors report that boundary placement and loss masking are jointly necessary for the gains.Source: §1 · p. 2

2 Preliminaries

The preliminaries place pause-token methods in a shared formulation and introduce GRPO. Pause-token methods vary by where pauses are inserted and whether pause targets contribute to the loss.

2 Preliminaries

  • The preliminaries establish a shared notation for masked-pause fine-tuning and situate prior pause-token methods within it.They locate differences among methods in the position selector and pause-loss treatment.Source: §2 · p. 2

2.1 Pause-token training

  • A position selector partitions a response into contiguous segments, between which pause tokens are inserted; pause-target positions are excluded from the loss while pauses remain in context.Setting the selector to empty recovers standard SFT; prior methods differ in placement and whether pause positions are masked.Source: §2.1 · pp. 2–3

2.2 GRPO

  • GRPO samples a group of responses per prompt, scores them, and uses group-relative advantages and per-token policy ratios to optimize the policy with a reference-policy KL penalty.The group-relative advantage standardizes each reward by the group's mean and standard deviation.Source: §2.2 · p. 3

3 Pilot Experiments

The pilot experiments examine what masked pauses change during adaptation and what boundary-adjacent representations encode. Their findings motivate mode retention and non-myopic compression as hypotheses for scaled tests.

3.1 Modular continual learning: a retention asymmetry

  • The continual-learning pilot sequentially trains on two disjoint regimes, then compares D2 adaptation and D1 retention under matched training conditions.The PAUSE condition inserts one loss-masked pause after every content token; NO PAUSE uses standard next-token prediction.Source: §3.1 · p. 3
  • At matched D2 fit, PAUSE ends near 1.5 nats of held-out D1 loss versus about 6 for NO PAUSE, a roughly 4× retention gap.Both conditions reach the same ∼10−7 D2 loss floor, and the retention gap is not explained by tokens processed per step.Source: §3.1 · p. 3, Figure 2 · p. 4
  • Masked-pause SFT yields current-step probe accuracy of 0.282 versus 0.133 for NO PAUSE, and next-step accuracy of 0.104 versus 0.062.The next-step accuracy is also above the base value of 0.075; unmasked pause targets preserve most of the effect, suggesting placement is primary and masking adds a smaller increment.Source: §3.2 · p. 4, Figure 3 · p. 4

3.3 From hypotheses to predictions

  • The pilots motivate tests of whether MBP improves math and code while preserving general capability, whether placement matters at matched pause budget, and whether GRPO retains wider behavioral support.The paper identifies these as scaled predictions based on H1 and H2.Source: §3.3 · pp. 4–5

4 A retention certificate for masked-pause SFT

The retention certificate bounds masked-pause SFT's protected drift on D1 logits relative to ordinary SFT. When the context-separation factor is below one, it yields a stronger retention guarantee.

4 A retention certificate for masked-pause SFT

  • When βctx < 1, masked-pause SFT inherits a stronger retention guarantee than ordinary SFT in the stated local linearized regime and matched D2 fitting setting.The certificate bounds masked-pause protected drift by βctx times ordinary SFT's certified protected drift.Source: §4 · p. 5
  • The condition βctx < 1 corresponds to sufficient Jacobian-based separation between pause-perturbed and ordinary prefix states, a structural property encoded by MBP's boundary selector.The certificate's setup and proofs are provided in Appendices B and C.Source: §4 · p. 5

5 Method

The method places loss-masked pause tokens at semantic boundaries to support mode retention and non-myopic compression. It also applies pauses during GRPO policy updates without changing rollouts or rewards.

5 Method

  • Masked Boundary Pause (MBP) training formalizes the pause strategy motivated by H1 and H2.MBP is tested across 1B–8B fine-tuning and GRPO settings.Source: §5 · p. 5

5.1 Masked Boundary Pause Training

  • MBP selects semantic-unit boundaries, such as sentences, reasoning steps, or code lines, for pause placement.The boundary selector specializes the position selector g to semantic units.Source: §5.1 · p. 5
  • Boundary placement is motivated as supporting mode retention and compression of a complete semantic unit at the preceding token.The proposed rationale is that pauses at step boundaries absorb structural learning and encourage implicit summarization.Source: §5.1 · p. 5
  • Placement comparisons use the same pause-token budget M to isolate the role of the selector g.The MBP loss uses the boundary selector.Source: §5.1 · p. 5

5.2 Extension to GRPO

  • In GRPO, pauses are added only for policy updates; sampled responses and rewards remain based on the clean policy.The objective applies at non-pause positions, with policy ratios and KL penalties computed under pause-perturbed prefixes; pauses stay outside the action space.Source: §5.2 · pp. 5–6

6 Experiment Setup

The experiments test downstream math and code supervised fine-tuning and GRPO-based reasoning training. SFT comparisons match the pause-token budget, while GRPO evaluation tracks benchmark performance and training dynamics.

6 Experiment Setup

  • Experiments test math/code supervised fine-tuning and GRPO-based reasoning training.The evaluation covers RQ1, RQ2, and RQ3.Source: §6 · p. 6

6.1 Downstream Math/Code SFT

  • SFT uses Qwen3 base models from 1.7B to 8B and Llama-3.1-8B-Instruct, with curated math and code data and pauses inserted only on responses.The study reports pass@1 on math and code benchmarks and pass@4 on general-capability benchmarks; 8B models use LoRA.Source: §6.1 · p. 6
  • SFT baselines include no pauses, prompt-response-boundary pauses, random pauses, low-likelihood-token pauses, and MBP at reasoning-step or code-line boundaries.All pause variants are loss-masked except MBP W/O MASKING, and matched pause-token budgets isolate placement from token count.Source: §6.1 · p. 6

6.2 GRPO Setup

  • GRPO compares standard training with MBP-GRPO on Qwen3-1.7B and 4B using 5K-problem subsets.It reports math pass@1, convergence AUC, and mixed-group ratio; Table 1 and Table 2 define math and code benchmark averages, respectively.Source: §6.2 · p. 6, Table 1 · p. 6, Table 2 · p. 6

7 Results

Across tested model variants, MBP improves math and code reasoning and preserves general capability; placement comparisons favor boundary placement. MBP-GRPO also improves final accuracy and convergence, with early rollout diversity accompanying its training advantage.

7.1 Scaling and Retention (RQ1)

  • On Qwen3-1.7B-Base, MBP’s general-benchmark average rises +4.2 after math and +2.5 after code over base, beating SFT by +3.1 and +4.8, respectively.The average covers MMLU, GPQA, HellaSwag, BBH, and PIQA; the findings support preserving pretrained capability while acquiring reasoning.Source: Table 3 · p. 7, §7.1 · p. 7
  • Math gains over SFT range from +1.7 to +6.3 points across tested Qwen3 and Llama variants, while code gains range from +0.6 to +2.5 across Qwen3.The improvement is consistent in sign across every tested model variant.Source: §7.1 · p. 7

7.2 Placement and Masking (RQ2)

  • At matched pause-token budgets, math results span 7.5 points across placements, from DIT’s −5.0 to MBP’s +2.5 relative to SFT; only MBP is reliably positive on both domains.Append and DIT help code but hurt math, while random is near-flat on code and negative on math.Source: §7.2 · p. 7, Table 4 · p. 7

7.3 Extension to GRPO exploration (RQ3)

  • MBP-GRPO raises final four-benchmark average accuracy by +1.31 points on Qwen3-1.7B and +0.40 on Qwen3-4B over standard GRPO, with normalized convergence AUC increases of +0.048 and +0.045.These results report both final accuracy and convergence speed.Source: §7.3 · p. 7
  • Figure 4 shows a higher early mixed-group ratio for MBP-GRPO, followed by a later decline consistent with earlier convergence on solved prompts.The early ratio advantage accompanies more useful update signals and reward and accuracy curves that pull ahead from the first steps.Source: §7.3 · pp. 7–8, Figure 4 · p. 8

8 Analysis

The retention analyses find that MBP supports adaptation while better preserving pretrained behavior, with masking and boundary placement shaping that effect. The non-myopic compression analysis finds that MBP boundaries encode more final-answer-related information than standard SFT boundaries.

8.1 Retention Analyses

  • MBP improves both base-correct preservation and new-solve rate, indicating adaptation while better preserving the pretrained distribution.BCP measures retained correctness on base-correct examples; NSR measures base-wrong examples made correct after fine-tuning.Source: §8.1 · p. 8, Table 6 · p. 8
  • Switching from masked to predicted pauses costs −2.6 points on math and −2.8 points on code; predicted boundary pauses fall below standard SFT on code.This ablation indicates that masking is load-bearing at boundary placement.Source: §8.1 · p. 8
  • MBP maintains higher entropy at step boundaries across in-domain and OOD tasks, while average entropy across all tokens remains almost identical to SFT.The pattern suggests selective preservation of distributional diversity at boundaries, alongside commitment to a distribution within steps.Source: §8.1 · pp. 8–9, Table 7 · p. 9

8.2 Non-Myopic Compression

  • MBP boundaries compress significantly more final-answer-related information than standard SFT boundaries on mathematical reasoning benchmarks.The MI proxy measures final-answer predictability from the intermediate step boundary.Source: §8.2 · p. 9, Table 8 · p. 9

9 Related Work

Prior work explains pause-token gains through added computation, direct generation cues, or focal-loss and boundary effects. This paper instead treats pause tokens as training-time context perturbations and applies masked semantic-boundary insertion to SFT and GRPO.

Related Work

  • Earlier pause-token accounts emphasize computational bandwidth or direct generation cues, whereas this work isolates their role as training-time context perturbations.Prior explanations include forward-pass computation and expressivity (Pfau et al., 2024; London and Kanade, 2025), focal-loss amplification, and structural anchoring at sentence boundaries.Source: §9 · p. 9
  • Pause-token methods have inserted tokens to delay generation, increase transformer expressivity, or guide processing at low-confidence positions and sentence boundaries.Examples include Goyal et al. (2024), Herel and Mikolov (2024), London and Kanade (2025), Kim et al. (2025), and Liu et al. (2026).Source: §9 · p. 9
  • MBP complements SFT and GRPO by inserting tokens at semantic boundaries and masking them from direct prediction to mitigate the adaptation–retention trade-off.The trade-off is that fine-tuning on a new reasoning task often degrades previously established language fluency and factual knowledge (Luo et al., 2025).Source: §9 · p. 9

10 Conclusions

The paper frames pause tokens as a fine-tuning-dynamics intervention, grounded in mode-retention and non-myopic-compression hypotheses. It reports improved math and code reasoning while preserving general capability, with boundary placement outperforming SFT across scales and retention helping RL exploration.

10 Conclusions

  • MBP improves math and code reasoning while preserving general capability on unrelated benchmarks.The paper reports that boundary placement reliably outperforms SFT across model scales and that retention also helps exploration in RL training.Source: §10 · p. 9
  • The conclusion presents mode retention and non-myopic compression as hypotheses motivating Masked Boundary Pause Training.The paper describes its perspective as a shift from computational expressivity to train-time retention and compression.Source: §10 · pp. 9–10
  • The authors propose this training-dynamics perspective as support for further research across domains and adaptation regimes.Source: §10 · p. 10

Limitations

This section examines the scope and rationale for masked pauses, reporting retention across sequential adaptations and a local first-order account while noting limits in correctness-based measurement and selected-prefix certification. It also explains the formal role of masking and boundary placement and documents training settings, compute resources, and math data.

Limitations

  • The study focuses on full fine-tuning and per-example correctness for retention, while initial LoRA experiments show more moderate gains and motivate further study of parameter-efficient adaptation and finer-grained retention.The reported measures are BCP and NSR; future work should examine token- or distribution-level retention and systematically test MBP under parameter-efficient adaptation.Source: Limitations · p. 10

A.2 Multi-stage continual learning

  • 3–4 nats: the held-out D1-loss retention gap favoring masked pauses persisted through all three later stages, was positive in 8/8 seeds at every stage, and did not reduce adaptation quality.The gap neither accumulated nor decayed across the chain; both conditions reached the same ∼10−7 adaptation floor on every Dk.Source: Table 9 · p. 13, §A.2 · p. 13

A.3 iGSM probing pilot

  • The iGSM probing pilot compares hidden-state probes for current-step and next-step answers at the period token before each reasoning-step boundary across four protocols.The supplied passages describe the probing procedure but do not provide its results.Source: §A.3 · p. 13

B.1 A first-order view of masked-pause mode retention

  • Masked-pause SFT has a stronger first-order retention certificate when βctx < 1, under a local linearized model with matched D2 fitting and ordinary D2 targets.The certificate preserves the D1 top token on selected no-pause prefixes; it does not establish preservation of the full D1 predictive distribution or realized retained fraction.Source: §B.1 · p. 14, C Theoretical analysis for… · p. 15

C.2 Direct supervised-prefix relocation

  • Masked pauses relocate direct supervision for the next ordinary token from the unpaused boundary to the pause-appended boundary, while masking prevents a direct pause-target gradient at the original boundary.Earlier tokens can still receive indirect gradients through the pause-state Jacobian; the analysis separates this carry contribution from pause-local overlap.Source: §B.1 · p. 14, §C.2 · p. 15

C.8 Why masking the pause-token loss matters

  • Masking removes the added loss term over original no-pause boundaries, which can erase the benefit of context perturbation, leaving Theorem B.3 clean.The passage attributes the clean result to removing this loss term.Source: §C.8 · p. 17

C.9 Pause insertion position

  • Reasoning-step boundaries are preferred because they can reduce carry and residual-feature pressures while keeping the next-step prediction budget moderate; overly sparse or dense insertion can weaken these conditions.The formal condition is βctx(A) < 1; the passage warns that sparse insertion leaves supervised prefixes close to SO, while unnatural dense insertion can increase λ(A), ν(A), or κ(A).Source: §C.9 · p. 18

D.1 Hyperparameter settings

  • Math and code SFT each used 3 epochs and batch size 32 with model-specific learning rates; GRPO used 1 episode, batch size 128, 8 rollouts per prompt, and KL coefficient 0.001.Across experiments, training used FusedAdam, CosineAnnealingLR, a 0.03 warmup ratio, and bf16 precision.Source: §D.1 · p. 18

D.2 Computation resources

  • Training experiments used OpenRLHF on 2× NVIDIA RTX PRO 6000 and 2× NVIDIA RTX A100 GPUs.Source: §D.2 · p. 18

D.3 Dataset Statistics

  • The math-domain data mixture included GSM8K, MATH, and DeepMATH; Table 13 summarizes the training and evaluation dataset statistics.Source: §D.3 · p. 18

D.4 Evaluation setup

This block documents benchmark scoring, implementation settings, pause-objective and preprocessing choices, the MI-proxy procedure, and the scope of supplementary SFT and GRPO results.

D.4 Evaluation setup

E Implementation Details

E.1 Objective function

  • Masked-pause SFT predicts ordinary answer tokens from prefixes that may contain pauses, while excluding pause-token targets from the loss; pause-predicted SFT supervises those targets.Source: §E.1 · p. 19

E.2 Step-Boundary Extraction

  • Deterministic preprocessing inserts one masked pause at each extracted reasoning-step boundary, preserving math environments and treating each retained code line as a step.Source: §E.2 · p. 19

E.3 Training Overhead

  • MBP adds about 16 pauses per math example and 27 per code example, corresponding to about 2.2% and 9.8% of tokens, respectively.Source: §E.3 · p. 19

E.4 MI-Proxy Computation

  • The MI-proxy procedure scores cumulative solution prefixes ending at step boundaries against the final balanced expression, then macro-averages over boundaries and examples.Source: §E.4 · pp. 19–20

E.5 Examples of Methods

F Additional Results

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