The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- and The Channel, Not the Content, Decides What Works
On the map: LLM reasoning → Reinforcement learning for reasoning
also in 1 more
Abstract (arXiv)
Dense per-step supervision is the standard remedy for sparse-reward long-horizon LLM agents: reward the policy for predicting its next observation, which looks provably safe under potential-based shaping. Published prediction-reward and auxiliary-loss variants report both successes and instabilities; we supply the controlled account: 74 preregistered arms dissect one fixed prediction signal under GRPO across ALFWorld, WebShop, a synthetic POMDP, and Qwen3-1.7B/4B/8B, varying only the delivery mechanism. (1) Every run sustaining this difference-form reward under untouched std normalization (no filtering, dynamic-sampling, or decoupling mitigations) collapses: eleven runs across scales, coefficients, group sizes, and groupings (the floor-bound synthetic environment stalls instead); ALFWorld runs end in an absorbing state (prediction accuracy -> 1.0, success -> 0): the optimizer builds the "dark room". The algebra is one line: in all-fail groups z-scoring cancels the shaping coefficient; removing only std normalization restores baseline parity. (2) A signal's danger is set by its within-group variance trajectory, plus hackability as a second axis; it retrodicts every reward-channel collapse and survives preregistered prospective tests. (3) The same signal as a teacher-forced auxiliary loss is harmless on ALFWorld at 4B, but the gain is not the signal's: content-free placebos as a class match or beat gold at both matched seeds (s0: 78.8 vs 68.6; s42: 67.9 vs 57.9); the auxiliary update is the regularizer. (4) At 8B the recipe turns bistable: gold full-weight locks two of three seeds; every content-free or reduced-weight arm stays healthy. No ALFWorld or WebShop reward-channel variant measurably beats its matched-normalization baseline and no gold signal measurably outperforms its content-free placebo: the delivery channel, not the content, decides; which channel is safe is regime-dependent.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 25 of its 66 references that are in the map. First posted on arXiv 23 Jul 2026.
Builds on
25 of 66 references.
Closest ancestors · 14
Same direction, newest first
- 2026ECHO: Terminal Agents Learn World Models for Free23 May
- 2026GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization8 Jan
- 2025Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoning22 Oct
- 2025MO-GRPO: Mitigating Reward Hacking of Group Relative Policy Optimization on Multi-Objective Problems26 Sep
- 2025Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning28 Aug
- 2025Uncalibrated Reasoning: GRPO Induces Overconfidence for Stochastic Outcomes15 Aug
- 2025BNPO: Beta Normalization Policy Optimization3 Jun
- 2025The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models28 May
- 2025Understanding R1-Zero-Like Training: A Critical Perspective26 Mar
- 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
- 2025Reinforcement Learning with Verifiable Rewards: GRPO's Effective Loss, Dynamics, and Success Amplification9 Mar
- 2025Process Reinforcement through Implicit Rewards3 Feb
2 more
Other references · 11
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 386
- 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
- 2024ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLcited by 228
- 2023Small-scale proxies for large-scale Transformer training instabilitiescited by 183
- 2025Reward Shaping to Mitigate Reward Hacking in RLHFcited by 88
- 2026Reinforcement World Model Learning for LLM-based Agentscited by 24
- 2026Policy and World Modeling Co-Training for Language Agentscited by 15
- 2024When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learningcited by 14
- 2024Potential-Based Reward Shaping For Intrinsic Motivationcited by 12
- 2026GRPO, Dr. GRPO, and DAPO Are Three Operations on One Number: The Group-Standard-Deviation Identitycited by 2
Built on by
Citing papers per year: 2026: 5.
5 papers.
- 2026Hints, Critics, and Teachers: Prior Injection for Sparse-Reward RL in Vision-Language Math Reasoningcited by 0
- 2026The Filter Metric is Safety-Critical: Phantom Advantages in Group-Relative RL under Shaped Rewardscited by 0
- 2026ProCredit: From Outcome Rewards to Progress Credit in Agentic Reinforcement Learningcited by 0
- 2026RELACE: retrospective likelihood-based action credit estimation for long-horizon language agentscited by 0
- 2026Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewardscited by 0
- 2026Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards13 Oct 2026
- 2026RELACE: retrospective likelihood-based action credit estimation for long-horizon language agents5 Oct 2026
- 2026ProCredit: From Outcome Rewards to Progress Credit in Agentic Reinforcement Learning23 Sep 2026
- 2026The Filter Metric is Safety-Critical: Phantom Advantages in Group-Relative RL under Shaped Rewards12 Sep 2026
- 2026Hints, Critics, and Teachers: Prior Injection for Sparse-Reward RL in Vision-Language Math Reasoning22 Aug 2026
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- 2026A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
- 2026Before the Rollout Ends: Early Terminal Reward Prediction for Long-horizon Coding Agents
- 2024The Dark Side of Rich Rewards: Understanding and Mitigating Noise in VLM Rewards
- 2026Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning
- 2026How Far Can Unsupervised RLVR Scale LLM Training?
- 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
- 2026Make Sparse Rewards Count: Density-Aware Reward Aggregation for Multi-Reward RL
- 2026Explore More, Drift Less: Outcome-Only Reinforcement Learning Can Suffice for Long-Horizon Interactive Agents