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arxiv: 2605.20256 · v1 · pith:SNFFGAUGnew · submitted 2026-05-18 · 💻 cs.LG · cs.AI

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

Xikai Zhang , Yongzhi Li , Likang Xiao , Yingze Zhang , Yanhua Cheng , Quan Chen , Peng Jiang , Wenjun Wu
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Liu Liu
This is my paper

Pith reviewed 2026-05-21 08:06 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords reinforcement learningfeedback-guided explorationbi-objective optimizationpolicy alignmentcapability cultivationGRPOflywheel effecttraining efficiency
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The pith

Environment feedback guides exploration while two reinforcing objectives accelerate reinforcement learning and raise its performance ceiling.

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

Standard rollout sampling in methods like GRPO conditions all samples on the same prompt, so difficult tasks produce few high-quality examples and training stalls without a clear gradient direction. FBOS-RL introduces Feedback-Guided Exploration Enhancement that uses environment signals to steer sampling toward more useful rollouts. It then trains the policy with two objectives, Exploitation-oriented Policy Alignment and Exploration-oriented Capability Cultivation, that are designed to strengthen each other. Experiments show this creates a positive flywheel that yields faster learning, a higher final performance level, greater policy entropy, and smaller gradient norms than GRPO or feedback baselines when the total number of rollouts is held fixed.

Core claim

FBOS-RL lets the model perform Feedback-Guided Exploration Enhancement based on environment feedback and then applies two mutually reinforcing training objectives: Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC). These components form a positive flywheel effect that improves both training efficiency and the final performance ceiling of reinforcement learning.

What carries the argument

Feedback-Guided Exploration Enhancement combined with the bi-objective pair of Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC) that produce mutual reinforcement.

If this is right

  • Under an identical number of rollouts FBOS-RL learns substantially faster than GRPO and feedback-based baselines.
  • FBOS-RL ultimately attains a higher performance ceiling than the compared methods.
  • The training process maintains higher policy entropy and lower gradient norms throughout.
  • The mutual reinforcement between EPA and ECC produces a flywheel that lifts both efficiency and final capability.

Where Pith is reading between the lines

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

  • If environment feedback stays reliable for weak policies, the same steering principle could be added to other rollout strategies beyond the GRPO family.
  • Sustained higher entropy during training may help long-horizon tasks that require continued exploration after initial progress.
  • Lower gradient norms could reduce the need for extra clipping or regularization in large-model RL pipelines.

Load-bearing premise

The environment feedback must be sufficiently informative and unbiased to steer sampling toward useful rollouts even when the current policy is weak.

What would settle it

Apply FBOS-RL in an environment that supplies noisy or systematically biased feedback and check whether it still learns faster and reaches a higher final score than GRPO under the same rollout budget.

Figures

Figures reproduced from arXiv: 2605.20256 by Likang Xiao, Liu Liu, Peng Jiang, Quan Chen, Wenjun Wu, Xikai Zhang, Yanhua Cheng, Yingze Zhang, Yongzhi Li.

Figure 1
Figure 1. Figure 1: An illustrative analogy of the rollout-sampling stage in vanilla GRPO-style RL: a monkey randomly hitting keys on a typewriter is highly unlikely to ever produce the works of Shakespeare. Likewise, when a prompt exceeds the policy’s current capability, simple sampling strategies rarely produce a high-quality rollout, leaving training without a meaningful gradient anchor. the universe ends, see [PITH_FULL_… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our Feedback-Driven Bi-Objective Synergistic RL (FBOS-RL) framework. In the sampling phase, the policy first generates n initial rollouts from the original prompt q; a rule-based verifier produces a natural-language feedback for each, which is then concatenated with q and the rollout to form a Feedback-Augmented Prompt (FAP) used for a second round of feedback-guided sampling. In the optimizati… view at source ↗
Figure 3
Figure 3. Figure 3: Performance on the validation set during training: our method (FBOS-RL) vs. vanilla GRPO. Left: final pass rate of the Llama-3.1-8B-Instruct model on TravelPlanner. Middle: final pass rate of the Qwen3-14B model on TravelPlanner. Right: validation score of the Qwen3.5-27B model on MiniF2F - Lean4. The bottom x-axis denotes the number of training steps, the top x-axis denotes the cumulative number of rollou… view at source ↗
Figure 4
Figure 4. Figure 4: Final pass rate of the Llama-3.1-8B-Instruct model on the TravelPlanner validation set during training, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method (FBOS-RL) is compared with vanilla GRPO. For the Qwen3-14B model, the results are shown in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Final pass rate of the Qwen3-14B model on the TravelPlanner validation set during training, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method (FBOS-RL) is compared with vanilla GRPO. We further report the following four metrics on the TravelPlanner validation set during training: Commonsense Constraint Pass Rate (Micro), Commonsense Constraint Pass Rate (Macro), Ha… view at source ↗
Figure 6
Figure 6. Figure 6: Commonsense and hard constraint pass rates of the Llama-3.1-8B-Instruct model on the TravelPlanner validation set during training: Commonsense Constraint Pass Rate (Micro) (a), Commonsense Constraint Pass Rate (Macro) (b), Hard Constraint Pass Rate (Micro) (c), and Hard Constraint Pass Rate (Macro) (d). Our method (FBOS-RL) is compared with vanilla GRPO. FBOS-RL (Ours) GRPO 0.8 0.6 0.4 0.2 0 25 50 75 100 1… view at source ↗
Figure 7
Figure 7. Figure 7: Commonsense and hard constraint pass rates of the Qwen3-14B model on the TravelPlanner validation set during training: Commonsense Constraint Pass Rate (Micro) (a), Commonsense Constraint Pass Rate (Macro) (b), Hard Constraint Pass Rate (Micro) (c), and Hard Constraint Pass Rate (Macro) (d). Our method (FBOS-RL) is compared with vanilla GRPO. Furthermore, as shown in [PITH_FULL_IMAGE:figures/full_fig_p009… view at source ↗
Figure 8
Figure 8. Figure 8: Actor entropy during training: our method (FBOS-RL) vs. vanilla GRPO. Left: Llama-3.1-8B-Instruct on TravelPlan￾ner. Middle: Qwen3-14B on TravelPlanner. Right: Qwen3.5-27B on MiniF2F - Lean4. Across all three settings, our method does not suffer from entropy collapse and consistently maintains higher entropy than vanilla GRPO. FBOS-RL (Ours) GRPO 2 1.5 1 0 0.5 50 100 150 200 250 300 350 400 FBOS-RL (Ours) … view at source ↗
Figure 9
Figure 9. Figure 9: Gradient norm during training: our method (FBOS-RL) vs. vanilla GRPO. Left: Llama-3.1-8B-Instruct on TravelPlanner. Middle: Qwen3-14B on TravelPlanner. Right: Qwen3.5-27B on MiniF2F - Lean4. Across all three settings, our method exhibits a lower gradient norm than vanilla GRPO, indicating better training stability. 0 20 40 60 GPQA Diamond Accuracy (%) before training after FBOS-RL (Ours) 33.3333.33 13.13 2… view at source ↗
Figure 10
Figure 10. Figure 10: OOD generalization to the GPQA-Diamond dataset: comparison of three models (Llama-3.1-8B-Instruct, Qwen3- 14B, and Qwen3.5-27B) before training and after training with our FBOS-RL method. The Llama-3.1-8B-Instruct and Qwen3-14B models are trained with FBOS-RL on the TravelPlanner dataset, while the Qwen3.5-27B model is trained with FBOS-RL on the MiniF2F - Lean4 dataset. None of the models are further tra… view at source ↗
Figure 11
Figure 11. Figure 11: Mean quality (left) and max quality (right) of rollouts generated by the Qwen3-14B model conditioned on the Feedback-Augmented Prompt (FAP) at each training step on the training set: our method vs. the baseline that only trains Objective 1 (EPA). In addition, we report results separately for each difficulty level. Mean quality of rollouts generated by the model conditioned on the Feedback-Augmented Prompt… view at source ↗
Figure 12
Figure 12. Figure 12: Mean quality of FAP-conditioned rollouts on the training set, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 1 (EPA). Max quality of rollouts generated by the model conditioned on the Feedback-Augmented Prompt (FAP): 1.4 1.2 1 25 50 75 100 125 150 175 200 225 1.6 1.8 2 250 1.4 1.2 1 25 50 75 100 125 150 175 200 225 1.… view at source ↗
Figure 13
Figure 13. Figure 13: Max quality of FAP-conditioned rollouts on the training set, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 1 (EPA). These figures show that introducing Objective 2 (ECC) leads the model to generate higher-quality rollouts under the FAP (both mean and max quality are higher, across every difficulty level). The figure … view at source ↗
Figure 14
Figure 14. Figure 14: Mean quality of rollouts generated during the entire sampling phase (initial sampling and FAP-guided second-round sampling combined) at each training step on the training set: our method vs. the baseline that only trains Objective 1 (EPA). Furthermore, we report the mean and max quality for each difficulty level. Mean quality at each difficulty level: FBOS-RL (Ours) FBOS-RL w/o ECC 1.4 1.2 1 0.8 25 50 75 … view at source ↗
Figure 15
Figure 15. Figure 15: Mean quality of rollouts generated during the sampling phase on the training set, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 1 (EPA). Max quality at each difficulty level: FBOS-RL (Ours) FBOS-RL w/o ECC 1.2 1 25 50 75 100 125 150 175 200 225 250 1.4 1.6 1.8 2 1.5 1.4 25 50 75 100 125 150 175 200 225 250 1.6 1.7 1.… view at source ↗
Figure 16
Figure 16. Figure 16: Max quality of rollouts generated during the sampling phase on the training set, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 1 (EPA). These figures show that introducing Objective 2 (ECC) significantly improves the quality of rollouts discovered by the model during the sampling phase. On the validation set, we obse… view at source ↗
Figure 17
Figure 17. Figure 17 [PITH_FULL_IMAGE:figures/full_fig_p012_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Final pass rate on the TravelPlanner validation set across different difficulty levels: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 1 (EPA). The figure below shows the following four metrics on the validation set during training: Commonsense Constraint Pass Rate - Micro, Commonsense Constraint Pass Rate - Macro, Hard Constraint Pass Rate - Micro, … view at source ↗
Figure 19
Figure 19. Figure 19: Commonsense and hard constraint pass rates (micro and macro) on the TravelPlanner validation set: our method vs. the baseline that only trains Objective 1 (EPA). This demonstrates that Objective 2 (ECC) can effectively boost Objective 1 (EPA). 4.3.2 Objective 1 (EPA) Boosts Objective 2 (ECC) We design a baseline that only optimizes Objective 2 (ECC) during training. The figure below reports, at each train… view at source ↗
Figure 20
Figure 20. Figure 20: Mean quality of rollouts generated during the entire sampling phase at each training step on the training set: our method vs. the baseline that only trains Objective 2 (ECC). In addition, we further report the mean quality at each difficulty level: FBOS-RL (Ours) FBOS-RL w/o EPA 1.2 1 0.8 0.6 25 50 75 100 125 150 175 200 225 250 1.4 1.6 1.8 2 FBOS-RL (Ours) FBOS-RL w/o EPA FBOS-RL (Ours) FBOS-RL w/o EPA 1… view at source ↗
Figure 21
Figure 21. Figure 21: Mean quality of rollouts generated during the sampling phase on the training set, broken down by difficulty level: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 2 (ECC). These figures show that introducing Objective 1 (EPA) significantly improves the quality of rollouts discovered by the model during the sampling phase. On the validation set, we obs… view at source ↗
Figure 22
Figure 22. Figure 22: Final pass rate on the TravelPlanner validation set: our method vs. the baseline that only trains Objective 2 (ECC). The figure below reports, during training, the final pass rate on the validation set for each difficulty level (“easy”, “medium”, “hard”). 0.6 0.4 0.2 25 50 75 100 125 150 175 200 225 250 0.8 0 FBOS-RL (Ours) FBOS-RL w/o EPA 0.6 0.4 0.2 25 50 75 100 125 150 175 200 225 250 0.8 0 FBOS-RL (Ou… view at source ↗
Figure 23
Figure 23. Figure 23: Final pass rate on the TravelPlanner validation set across different difficulty levels: easy (left), medium (middle), and hard (right). Our method vs. the baseline that only trains Objective 2 (ECC). 14 [PITH_FULL_IMAGE:figures/full_fig_p014_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Commonsense and hard constraint pass rates (micro and macro) on the TravelPlanner validation set: our method vs. the baseline that only trains Objective 2 (ECC). This demonstrates that Objective 1 (EPA) can effectively boost Objective 2 (ECC). 4.4. Controlling for the Number of Parameter Updates Since our method performs two parameter updates per training step, while standard GRPO performs only one parame… view at source ↗
Figure 25
Figure 25. Figure 25: Final pass rate of the Qwen3-14B model on the TravelPlanner validation set: our method vs. the GRPO w/ Extra Update baseline. The results across different difficulty levels are as follows: 0.6 0.4 0.2 25 50 75 100 125 150 175 200 225 250 0.8 0 FBOS-RL (Ours) GRPO GRPO w/ Extra Update FBOS-RL (Ours) GRPO GRPO w/ Extra Update FBOS-RL (Ours) GRPO GRPO w/ Extra Update 0.6 0.4 0.2 25 50 75 100 125 150 175 200 … view at source ↗
Figure 26
Figure 26. Figure 26: Final pass rate of the Qwen3-14B model on the TravelPlanner validation set across different difficulty levels: easy (left), medium (middle), and hard (right). Our method vs. the GRPO w/ Extra Update baseline. The four constraint pass rate metrics on the validation set are reported below: 0.6 0.4 0.2 25 50 75 100 125 150 175 200 225 250 0.8 0 b) 0.8 0.7 0.6 25 50 75 100 125 150 175 200 225 250 0.9 0 a) FBO… view at source ↗
Figure 27
Figure 27. Figure 27: Commonsense and hard constraint pass rates (micro and macro) of the Qwen3-14B model on the TravelPlanner validation set: our method vs. the GRPO w/ Extra Update baseline. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_27.png] view at source ↗
Figure 28
Figure 28. Figure 28: Final pass rate of the Llama-3.1-8B-Instruct model on the TravelPlanner validation set: our method vs. the GRPO w/ Extra Update baseline. The results across different difficulty levels are reported in [PITH_FULL_IMAGE:figures/full_fig_p017_28.png] view at source ↗
Figure 29
Figure 29. Figure 29: Final pass rate of the Llama-3.1-8B-Instruct model on the TravelPlanner validation set across different difficulty levels: easy (left), medium (middle), and hard (right). Our method vs. the GRPO w/ Extra Update baseline. The four constraint pass rate metrics on the validation set are reported in [PITH_FULL_IMAGE:figures/full_fig_p017_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Commonsense and hard constraint pass rates (micro and macro) of the Llama-3.1-8B-Instruct model on the TravelPlanner validation set: our method vs. the GRPO w/ Extra Update baseline. Consistent with the observations on the Qwen3-14B model, on the Llama-3.1-8B-Instruct model our method also significantly outperforms the GRPO w/ Extra Update baseline across all metrics and difficulty levels, further confirm… view at source ↗
Figure 31
Figure 31. Figure 31: Training dynamics of the EPA objective on Llama-3.1-8B-Instruct. (a) Training-set score of EPA steadily increases along training steps. (b) The corresponding std steadily decreases, indicating that EPA can be optimized in a stable manner. 50 100 150 200 250 300 350 400 1 0.5 0 -0.5 1.5 2 Training Steps Score 50 100 150 200 250 300 350 400 1 0.5 0 -0.5 1.5 2 Training Steps Score 50 100 150 200 250 300 350 … view at source ↗
Figure 32
Figure 32. Figure 32: Training-set score of the EPA objective on Llama-3.1-8B-Instruct, broken down by training-sample difficulty (easy, medium, hard, from left to right). The score consistently rises along training steps across all three difficulty levels. 25 50 75 100 125 150 175 200 225 250 1.4 FBOS-RL (Ours) 1.2 1 0.8 1.6 1.8 Training Steps Score 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 0.3 0.2 0.1 0.4 0.5 0.6 T… view at source ↗
Figure 33
Figure 33. Figure 33: Training dynamics of the EPA objective on Qwen3-14B. (a) Training-set score of EPA steadily increases along training steps. (b) The corresponding std steadily decreases, indicating that EPA can be optimized in a stable manner. 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 1.4 1.2 1 0.8 1.6 1.8 2 Training Steps Score 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 1.4 1.2 1 0.8 1.6 1.8 2 Training… view at source ↗
Figure 34
Figure 34. Figure 34: Training-set score of the EPA objective on Qwen3-14B, broken down by training-sample difficulty (easy, medium, hard, from left to right). The score consistently rises along training steps across all three difficulty levels. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_34.png] view at source ↗
Figure 35
Figure 35. Figure 35: Training dynamics of the ECC objective on Llama-3.1-8B-Instruct. (a) Training-set score of ECC steadily increases along training steps. (b) The corresponding std steadily decreases, indicating that ECC can be optimized in a stable manner. 50 100 150 200 250 300 350 400 1 0.5 0 -0.5 1.5 2 Training Steps Score FBOS-RL (Ours) 50 100 150 200 250 300 350 400 1 -1 0.5 0 -0.5 1.5 2 Training Steps Score 50 100 15… view at source ↗
Figure 36
Figure 36. Figure 36: Training-set score of the ECC objective on Llama-3.1-8B-Instruct, broken down by training-sample difficulty (easy, medium, hard, from left to right). The score consistently rises along training steps across all three difficulty levels. 25 50 75 100 125 150 175 200 225 250 1.4 FBOS-RL (Ours) 1.2 1 0.8 1.6 1.8 2 Training Steps Score 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 0.3 0.2 0.1 0 0.4 0.5 0… view at source ↗
Figure 37
Figure 37. Figure 37: Training dynamics of the ECC objective on Qwen3-14B. (a) Training-set score of ECC steadily increases along training steps. (b) The corresponding std steadily decreases, indicating that ECC can be optimized in a stable manner. 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 1.4 1.2 1 0.8 1.6 1.8 2 Training Steps Score 25 50 75 100 125 150 175 200 225 250 FBOS-RL (Ours) 1.4 1.2 1 0.8 1.6 1.8 2 Training… view at source ↗
Figure 38
Figure 38. Figure 38: Training-set score of the ECC objective on Qwen3-14B, broken down by training-sample difficulty (easy, medium, hard, from left to right). The score consistently rises along training steps across all three difficulty levels. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_38.png] view at source ↗
read the original abstract

Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models. At its core, the training loop of GRPO and its variants alternates between rollout sampling and policy update. Unlike supervised learning, where each gradient step is anchored to an explicit ground-truth target, the optimal gradient direction for updating model parameters in this setting is not known a priori; the high-quality rollouts drawn during the sampling stage therefore act as the implicit "teacher" that guides every parameter update. However, GRPO adopt a simple sampling scheme that conditions all rollouts on the same original prompt. When a task lies beyond the policy model's current capability, this sampling scheme rarely yields a high-quality rollout, leaving the policy model without a meaningful gradient direction when updating its parameters, which causes training to stall. To address this issue, we propose FBOS-RL, a Feedback-Driven Bi-Objective Synergistic reinforcement learning framework. Specifically, we let the model perform Feedback-Guided Exploration Enhancement based on the feedback provided by the environment, and on top of this we design two mutually reinforcing training objectives: Exploitation-oriented Policy Alignment(EPA) and Exploration-oriented Capability Cultivation(ECC). Extensive experiments demonstrate that EPA and ECC can mutually reinforce each other, forming a positive flywheel effect that significantly improves both the training efficiency and the final performance ceiling of reinforcement learning. Specifically, under an identical number of rollouts, FBOS-RL learns substantially faster than GRPO and feedback-based baselines and ultimately attains a higher performance ceiling, while exhibiting higher policy entropy and lower gradient norms throughout training.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes FBOS-RL, a feedback-driven bi-objective synergistic RL framework for improving training of large models. It augments the GRPO loop with Feedback-Guided Exploration Enhancement that conditions rollout sampling on environment feedback, plus two mutually reinforcing objectives: Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC). The central claim is that EPA and ECC form a positive flywheel, yielding faster learning, higher performance ceilings, higher policy entropy, and lower gradient norms than GRPO and feedback baselines under a fixed rollout budget.

Significance. If the empirical claims hold under rigorous controls, the work offers a concrete mechanism for breaking the 'no high-quality rollout' stall in early RL training of LLMs. The explicit design of mutually reinforcing exploitation and exploration objectives, together with the reported entropy and gradient-norm diagnostics, provides a falsifiable account of the flywheel effect that could influence subsequent sample-efficient RL methods.

major comments (2)
  1. [§3.2] §3.2 (Feedback-Guided Exploration Enhancement): The premise that environment feedback remains sufficiently informative and unbiased to steer sampling toward useful rollouts even from a weak initial policy is stated without supporting controls. When rewards are sparse or binary, guided sampling reduces to near-random selection among poor trajectories; this directly undermines the claimed positive interaction between EPA and ECC and the resulting flywheel. No ablation varying feedback informativeness or initial policy strength is reported.
  2. [§4] §4 (Experiments): The abstract and results claim substantially faster learning and higher ceilings under identical rollout counts, yet the manuscript supplies no error bars, statistical tests, or sensitivity analysis to hyper-parameters. Without these, it is impossible to determine whether the reported advantages are robust or sensitive to the very feedback quality that the method assumes.
minor comments (2)
  1. [§3.3] Notation for the two objectives (EPA, ECC) is introduced without an explicit joint loss equation; a single combined objective formula would clarify how the bi-objective synergy is implemented.
  2. [Figure 3] Figure captions for training curves should explicitly state the number of random seeds and whether shaded regions represent standard deviation or standard error.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the assumptions underlying FBOS-RL and improve the empirical presentation. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Feedback-Guided Exploration Enhancement): The premise that environment feedback remains sufficiently informative and unbiased to steer sampling toward useful rollouts even from a weak initial policy is stated without supporting controls. When rewards are sparse or binary, guided sampling reduces to near-random selection among poor trajectories; this directly undermines the claimed positive interaction between EPA and ECC and the resulting flywheel. No ablation varying feedback informativeness or initial policy strength is reported.

    Authors: We appreciate this observation. The Feedback-Guided Exploration Enhancement relies on environment feedback to bias rollout selection, and our experiments span tasks with varying reward density. However, we acknowledge that explicit controls for feedback quality and initial policy strength would provide stronger validation of the flywheel mechanism. In the revised manuscript we will add ablations that (i) start from weaker initial checkpoints and (ii) inject controlled noise into the feedback signal to simulate reduced informativeness, thereby testing robustness of the EPA-ECC interaction. revision: yes

  2. Referee: [§4] §4 (Experiments): The abstract and results claim substantially faster learning and higher ceilings under identical rollout counts, yet the manuscript supplies no error bars, statistical tests, or sensitivity analysis to hyper-parameters. Without these, it is impossible to determine whether the reported advantages are robust or sensitive to the very feedback quality that the method assumes.

    Authors: We agree that statistical rigor and sensitivity analysis are necessary to substantiate the claims. The current results are reported from single runs without error bars or formal tests. In the revision we will rerun the main experiments with multiple random seeds, report mean and standard deviation, include statistical significance tests between FBOS-RL and baselines, and add sensitivity plots for the EPA/ECC weighting coefficient and feedback guidance strength. These additions will directly address concerns about robustness to feedback quality. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation or claims

full rationale

The paper introduces FBOS-RL by describing a Feedback-Guided Exploration Enhancement step followed by two new objectives (EPA and ECC) whose mutual reinforcement is asserted and then validated through experimental comparisons against GRPO and feedback baselines. No equations, parameter-fitting procedures, or self-citations are shown that would make any claimed prediction or result equivalent to its own inputs by construction. The central performance claims rest on rollout counts, entropy, and gradient-norm measurements rather than on any self-definitional loop or renamed fitted quantity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the standard RL assumption that environment feedback can be turned into useful exploration signals and on the modeling choice that two separate objectives can be optimized jointly without destructive interference.

axioms (1)
  • domain assumption Environment feedback is reliable enough to guide exploration when the policy is weak.
    Invoked in the definition of Feedback-Guided Exploration Enhancement.

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Lean theorems connected to this paper

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  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
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    Relation between the paper passage and the cited Recognition theorem.

    FBOS-RL performs Feedback-Guided Exploration Enhancement … two mutually reinforcing training objectives: Exploitation-oriented Policy Alignment (EPA) and Exploration-oriented Capability Cultivation (ECC) … positive bootstrapping flywheel

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Reference graph

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