R$^3$L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification

Jiajie Xu; Weijie Shi; Xiaofang Zhou; Xuchen Pan; Yaliang Li; Yanxi Chen; Yuchang Sun; Zexi Li

arxiv: 2601.03715 · v2 · pith:DOSNAH2Knew · submitted 2026-01-07 · 💻 cs.LG · cs.AI

R³L: Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification

Weijie Shi , Yanxi Chen , Zexi Li , Xuchen Pan , Yuchang Sun , Jiajie Xu , Xiaofang Zhou , Yaliang Li This is my paper

Pith reviewed 2026-05-25 07:44 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords Reinforcement LearningLarge Language ModelsReasoning TasksAgentic TasksCredit AssignmentExplorationTrajectory SynthesisLanguage Feedback

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The pith

R³L improves LLM reasoning and agentic performance by synthesizing better trajectories through reflect-then-retry, then refining updates with pivotal credit and positive amplification.

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

Reinforcement learning for large language models encounters poor exploration on hard tasks because repeated full rollouts rarely succeed and cost too much, while exploitation suffers from trajectory-level rewards that blame correct early steps for later mistakes and from training batches dominated by failures. R³L counters both problems by replacing blind sampling with reflect-then-retry: the model generates language feedback on its own errors, diagnoses where the attempt diverged, and restarts the rollout from that point to produce corrected trajectories. Pivotal credit assignment then restricts gradient updates to only the suffix after the divergence, leaving the shared correct prefix untouched, and positive amplification raises the weight of the newly successful trajectories so they steer optimization despite the prevalence of failures. These changes together deliver relative gains of 5 to 52 percent on agentic and reasoning benchmarks while preserving training stability.

Core claim

R³L synthesizes high-quality trajectories via reflect-then-retry that leverages language feedback to diagnose errors and restart from identified failure points, applies pivotal credit assignment to update only the diverging suffix where contrastive signals exist, and employs positive amplification to upweight successful trajectories, thereby overcoming low success rates, coarse credit assignment, and instability in standard RL for LLMs and producing 5 to 52 percent relative improvements on agentic and reasoning tasks.

What carries the argument

Reflect-then-retry synthesis that uses self-generated language feedback to convert failed trajectories into successful ones by restarting from diagnosed error points, paired with pivotal credit assignment that limits updates to error suffixes and positive amplification that boosts successful examples.

If this is right

Rollout costs fall because retries begin at localized failure points instead of restarting from scratch each time.
Credit assignment becomes finer-grained by excluding valid prefixes from gradient updates.
Training stability holds even though reflect-then-retry generates off-policy data.
Positive signals dominate optimization on tasks where failures are the majority outcome.

Where Pith is reading between the lines

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

The same language-feedback loop could be tested on sequential decision problems outside language models where internal error descriptions are available.
Relying on the model's own diagnostic language may lower dependence on external reward models or human labels in future RL pipelines.
The method suggests that restarting at the first divergence point could be combined with other credit-assignment techniques to further reduce variance in long-horizon tasks.

Load-bearing premise

Language feedback generated by the model itself can accurately diagnose its own errors and turn failed trajectories into successful ones without introducing new biases or needing outside supervision.

What would settle it

An experiment in which reflect-then-retry produces no increase in successful trajectories or introduces measurable bias into the training distribution would falsify the central claim.

Figures

Figures reproduced from arXiv: 2601.03715 by Jiajie Xu, Weijie Shi, Xiaofang Zhou, Xuchen Pan, Yaliang Li, Yanxi Chen, Yuchang Sun, Zexi Li.

**Figure 1.** Figure 1: Comparison between standard RL (GRPO) and R3L. Red blocks indicate erroneous steps, Green blocks indicate correct steps, and Gray blocks indicate masked prefix excluded from gradient updates. Standard RL suffers from (C1) inefficient stochastic sampling, (C2) valid prefix penalization, and (C3) gradient asymmetry due to failure dominance. R3L addresses these via (S1) reflect-then-retry for active explora… view at source ↗

**Figure 2.** Figure 2: Overview of the R3L framework. The method utilizes Language-Guided Reflect-Then-Retry to synthesize high-reward trajectories via verbal feedback. To preserve valid steps, Pivotal Credit Assignment masks shared prefixes to isolate critical decision points, while Positive Amplification scales the advantages of successful trajectories to stabilize off-policy training. 4 Methodology As illustrated in [PITH_FU… view at source ↗

**Figure 3.** Figure 3: Evolution of exploration metrics across envi [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Training Dynamics and Stability Analysis on ALFWorld. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Four types of trajectories in R3L. Type 1 represents base exploration from the current policy. Type 2 captures the reflection process that diagnoses errors and identifies pivot points. Type 3 shows retry generation conditioned on diagnostic guidance. Type 4 is the distillation trajectory that combines the original prefix with the corrected suffix, removing guidance dependency for training. D.3 Context Dist… view at source ↗

**Figure 6.** Figure 6: Evolution of average pivot points across train [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗

**Figure 7.** Figure 7: ALFWorld case study showing base trajectory failure and successful retry. The base trajectory exhausts [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗

**Figure 8.** Figure 8: DAPO case study showing base trajectory failure and successful retry. The base trajectory exhausts three [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗

**Figure 9.** Figure 9: System prompt used for the ALFWorld environment. [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗

**Figure 10.** Figure 10: System prompt used for the WebShop environment. [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗

**Figure 11.** Figure 11: System prompt used for the ScienceWorld environment. [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗

**Figure 12.** Figure 12: System prompt used for mathematical reasoning tasks. [PITH_FULL_IMAGE:figures/full_fig_p025_12.png] view at source ↗

**Figure 13.** Figure 13: The unified reflection prompt template used across all tasks. [PITH_FULL_IMAGE:figures/full_fig_p025_13.png] view at source ↗

**Figure 14.** Figure 14: The guidance prompt template used during the retry phase. The full JSON output from the reflection step [PITH_FULL_IMAGE:figures/full_fig_p026_14.png] view at source ↗

read the original abstract

Reinforcement learning drives recent advances in LLM reasoning and agentic capabilities, yet current approaches struggle with both exploration and exploitation. Exploration suffers from low success rates on difficult tasks and high costs of repeated rollouts from scratch. Exploitation suffers from coarse credit assignment and training instability: Trajectory-level rewards penalize valid prefixes for later errors, and failure-dominated groups overwhelm the few positive signals, leaving optimization without constructive direction. To this end, we propose R$^3$L, Reflect-then-Retry Reinforcement Learning with Language-Guided Exploration, Pivotal Credit, and Positive Amplification. To synthesize high-quality trajectories, R$^3$L shifts from stochastic sampling to active synthesis via reflect-then-retry, leveraging language feedback to diagnose errors, transform failed attempts into successful ones, and reduce rollout costs by restarting from identified failure points. With errors diagnosed and localized, Pivotal Credit Assignment updates only the diverging suffix where contrastive signals exist, excluding the shared prefix from gradient update. Since failures dominate on difficult tasks and reflect-then-retry produces off-policy data, risking training instability, Positive Amplification upweights successful trajectories to ensure positive signals guide the optimization process. Experiments on agentic and reasoning tasks demonstrate 5\% to 52\% relative improvements over baselines while maintaining training stability. Our code is released at https://github.com/shiweijiezero/R3L.

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

R³L combines language reflection to repair failed trajectories, suffix-only credit, and positive amplification to cut exploration costs and stabilize RL for LLM agents, but the abstract gives no experimental details to check if any of it works.

read the letter

The paper's main contribution is R³L, which uses the LLM's own language output to reflect on why a trajectory failed, then retries by starting from the point where it went wrong instead of from scratch. It pairs this with pivotal credit assignment that only applies gradients to the part after the error, and positive amplification to give more weight to the successful cases when failures are common. The abstract says this leads to 5 to 52 percent better performance on agent and reasoning tasks while keeping training stable. This is a direct response to two common headaches in applying RL to LLMs: the expense of generating many full trajectories that mostly fail, and the difficulty of figuring out which parts of a long sequence actually caused the outcome. By leveraging language feedback for diagnosis and repair, it tries to make exploration more targeted. The code release is helpful for anyone wanting to try it out. That said, the description is limited to the abstract, so the experimental support is not visible. We don't see what the baselines were, how many tasks were tested, whether the improvements are consistent, or any ablations on the three components. The biggest question is whether the reflect step actually produces useful corrections. If the model is bad at spotting its own mistakes, then the retry trajectories could be off-policy in a bad way, and the amplification might just reinforce flawed reasoning. The stress-test concern about unreliable self-diagnosis is a real one and isn't addressed in the summary. Readers working on improving RL for chain-of-thought or tool-using agents would find this relevant. It engages honestly with the practical constraints of these systems. It should go to peer review because the idea is concrete enough that referees can check the details and see if the gains are real or depend on specific prompting tricks.

Referee Report

2 major / 0 minor

Summary. The paper proposes R³L, a reinforcement learning framework for LLM reasoning and agentic tasks. It introduces reflect-then-retry to synthesize trajectories via language-guided error diagnosis and retry from failure points, pivotal credit assignment to update only diverging suffixes, and positive amplification to upweight successful trajectories amid failure-dominated data. The central empirical claim is 5%–52% relative gains over baselines on agentic and reasoning tasks with maintained training stability; code is released.

Significance. If the empirical claims hold under rigorous verification, the method could improve sample efficiency and stability in RL for LLMs by addressing exploration costs and coarse credit assignment. The explicit code release is a strength that enables direct reproducibility checks.

major comments (2)

[Abstract] Abstract: the reported 5%–52% relative improvements are presented without any description of baselines, number of runs, statistical tests, or ablation results. This absence makes it impossible to determine whether the gains are attributable to the three proposed components or to uncontrolled factors such as prompt engineering or post-hoc trajectory selection.
[Abstract] Abstract: the reflect-then-retry procedure assumes that the model’s own language feedback can accurately localize errors, propose corrective actions that succeed on retry, and avoid introducing new biases. No analysis, human evaluation, or proxy metric of reflection quality is supplied; if this assumption fails on hard tasks where failures dominate, the synthesized trajectories and subsequent pivotal-credit updates risk amplifying flawed signals rather than providing constructive direction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on the abstract and the reflect-then-retry mechanism. We address each point below and will make targeted revisions to improve clarity and provide additional supporting evidence.

read point-by-point responses

Referee: [Abstract] Abstract: the reported 5%–52% relative improvements are presented without any description of baselines, number of runs, statistical tests, or ablation results. This absence makes it impossible to determine whether the gains are attributable to the three proposed components or to uncontrolled factors such as prompt engineering or post-hoc trajectory selection.

Authors: The abstract is a concise summary; full details on baselines (standard RL methods for LLMs such as PPO variants), number of runs (multiple random seeds with reported variance), statistical considerations, and component ablations are provided in Sections 4 and 5. Relative gains are computed from these controlled comparisons on identical tasks and prompts. We will revise the abstract to briefly note the evaluation protocol and that ablations isolate the contributions of reflect-then-retry, pivotal credit, and positive amplification. revision: yes
Referee: [Abstract] Abstract: the reflect-then-retry procedure assumes that the model’s own language feedback can accurately localize errors, propose corrective actions that succeed on retry, and avoid introducing new biases. No analysis, human evaluation, or proxy metric of reflection quality is supplied; if this assumption fails on hard tasks where failures dominate, the synthesized trajectories and subsequent pivotal-credit updates risk amplifying flawed signals rather than providing constructive direction.

Authors: We agree that direct validation of reflection quality would strengthen the claims. The manuscript shows indirect support via higher overall success rates and stable training when reflect-then-retry is used versus standard sampling. A dedicated analysis (e.g., retry success rates or error-localization proxies) is not currently included. We will add quantitative proxy metrics on reflection effectiveness in the revision to address concerns about potential amplification of flawed signals. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic procedure is self-contained without reductions to fitted inputs or self-citations.

full rationale

The paper presents R³L as a new procedural algorithm consisting of reflect-then-retry synthesis, pivotal credit assignment, and positive amplification. No equations, parameters, or derivations are provided that reduce claimed performance gains to quantities defined by the method's own outputs or fitted values. The description relies on external experimental validation rather than internal self-definition or self-citation chains for its core claims. The method's assumptions about language feedback quality are stated as design choices but do not create a circular derivation where predictions equal inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, derivations, or modeling choices, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5811 in / 1161 out tokens · 26633 ms · 2026-05-25T07:44:09.996234+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agents
cs.LG 2026-04 unverdicted novelty 7.0

TCOD stabilizes on-policy distillation for multi-turn agents via temporal curriculum on trajectory depth, improving performance up to 18 points over vanilla OPD and sometimes surpassing the teacher.
STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning
cs.LG 2026-05 unverdicted novelty 6.0

STRIDE co-trains generator and verifier on outcome rewards alone to deliver learnable stepwise language feedback that redirects LLM reasoning trajectories and outperforms scalar-reward baselines.

Reference graph

Works this paper leans on

17 extracted references · 14 canonical work pages · cited by 2 Pith papers · 2 internal anchors

[1]

Group-in-Group Policy Optimization for LLM Agent Training

Group-in-group policy optimization for llm agent training.arXiv preprint arXiv:2505.10978. Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, and 1 others. 2024. Omni- math: A universal olympiad level mathematic bench- mark for large language models.arXiv preprint arXiv:2410.07985. Daya Guo, D...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[2]

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction

Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui- Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, and 1 others. 2025. Goedel- prover-v2: Scaling formal theorem proving with scaffolded data synthesis and self-correction.arXiv preprint arX...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[3]

Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez

Training language models with language feed- back.arXiv preprint arXiv:2204.14146. Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. 2023. Training language mod- els with language feedback at scale.arXiv preprint arXiv:2303.16755. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, X...

work page arXiv 2023
[4]

InACL, pages 9426–9439

Math-shepherd: Verify and reinforce llms step- by-step without human annotations. InACL, pages 9426–9439. Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. 2022. Scienceworld: Is your agent smarter than a 5th grader?arXiv preprint arXiv:2203.07540. Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun Li, Ji- aqing Liang, Sihang Jiang, Z...

work page arXiv 2022
[5]

but not both

However, it suffers from premature stagna- tion, plateauing at a reward of approximately 0.4 with visible variance. In contrast, R3L undergoes a warm-up phase due to initial adaptation to the reflection mechanism. Following step 120, R 3L demonstrates a rapid phase transition with a signif- icantly steeper learning curve, quickly surpassing the baseline a...

work page arXiv 2025
[6]

Start/Index page - Initial page with search functionality and task instruction

work page
[7]

Search Results page - Lists products returned by search engine with pagination

work page
[8]

Item page - Shows product details, options, and purchase button

work page
[9]

Item Sub-page - Shows additional product information
[10]

Done page - Final confirmation page after purchase ## Available Actions:

work page
[11]

search[your_query_here] - To search for products from any page with a search bar
[12]

24 ScienceWorld System Prompt You are an agent, your job is to do some scientific experiment in a virtual text-based environment

click[exact_button_text_here] - To click on any clickable element ## Task Completion: Goal: Find and purchase an item matching the given instruction within 15 steps Success: Episode ends when you click "Buy Now" with appropriate product and options Figure 10: System prompt used for the WebShop environment. 24 ScienceWorld System Prompt You are an agent, y...

work page
[13]

Carefully read and understand the problem
[14]

Show your reasoning step by step in the <think> tags

work page
[15]

Provide your final answer in the <answer> tags

work page
[16]

For numerical answers, provide the exact value

work page
[17]

why" questioning, trace issues back to their fundamental flawed assumptions or mental models. Please output in the following JSON format: {

If the problem asks for a specific format, use that format in your answer Figure 12: System prompt used for mathematical reasoning tasks. Unified Reflection Prompt Template You are a Reflector that analyzes trajectory logs based on user and environment feedback. Your goal is to identify what went wrong, trace root causes, and extract reusable principles f...

work page

[1] [1]

Group-in-Group Policy Optimization for LLM Agent Training

Group-in-group policy optimization for llm agent training.arXiv preprint arXiv:2505.10978. Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, and 1 others. 2024. Omni- math: A universal olympiad level mathematic bench- mark for large language models.arXiv preprint arXiv:2410.07985. Daya Guo, D...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[2] [2]

Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction

Let’s verify step by step. InThe Twelfth Inter- national Conference on Learning Representations. Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui- Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, and 1 others. 2025. Goedel- prover-v2: Scaling formal theorem proving with scaffolded data synthesis and self-correction.arXiv preprint arX...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[3] [3]

Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez

Training language models with language feed- back.arXiv preprint arXiv:2204.14146. Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. 2023. Training language mod- els with language feedback at scale.arXiv preprint arXiv:2303.16755. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, X...

work page arXiv 2023

[4] [4]

InACL, pages 9426–9439

Math-shepherd: Verify and reinforce llms step- by-step without human annotations. InACL, pages 9426–9439. Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. 2022. Scienceworld: Is your agent smarter than a 5th grader?arXiv preprint arXiv:2203.07540. Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun Li, Ji- aqing Liang, Sihang Jiang, Z...

work page arXiv 2022

[5] [5]

but not both

However, it suffers from premature stagna- tion, plateauing at a reward of approximately 0.4 with visible variance. In contrast, R3L undergoes a warm-up phase due to initial adaptation to the reflection mechanism. Following step 120, R 3L demonstrates a rapid phase transition with a signif- icantly steeper learning curve, quickly surpassing the baseline a...

work page arXiv 2025

[6] [6]

Start/Index page - Initial page with search functionality and task instruction

work page

[7] [7]

Search Results page - Lists products returned by search engine with pagination

work page

[8] [8]

Item page - Shows product details, options, and purchase button

work page

[9] [9]

Item Sub-page - Shows additional product information

[10] [10]

Done page - Final confirmation page after purchase ## Available Actions:

work page

[11] [11]

search[your_query_here] - To search for products from any page with a search bar

[12] [12]

24 ScienceWorld System Prompt You are an agent, your job is to do some scientific experiment in a virtual text-based environment

click[exact_button_text_here] - To click on any clickable element ## Task Completion: Goal: Find and purchase an item matching the given instruction within 15 steps Success: Episode ends when you click "Buy Now" with appropriate product and options Figure 10: System prompt used for the WebShop environment. 24 ScienceWorld System Prompt You are an agent, y...

work page

[13] [13]

Carefully read and understand the problem

[14] [14]

Show your reasoning step by step in the <think> tags

work page

[15] [15]

Provide your final answer in the <answer> tags

work page

[16] [16]

For numerical answers, provide the exact value

work page

[17] [17]

why" questioning, trace issues back to their fundamental flawed assumptions or mental models. Please output in the following JSON format: {

If the problem asks for a specific format, use that format in your answer Figure 12: System prompt used for mathematical reasoning tasks. Unified Reflection Prompt Template You are a Reflector that analyzes trajectory logs based on user and environment feedback. Your goal is to identify what went wrong, trace root causes, and extract reusable principles f...

work page