When Does Model Collapse Occur in Structured Interactive Learning?
This paper formalizes model collapse in multi-model interactive learning environments where models are trained on each other's synthetic outputs. The authors represent model interactions as directed graphs and derive necessary and sufficient conditions for collapse based on interaction graph topology. Finite-sample results for linear regression and asymptotic guarantees for general M-estimators are established, supported by numerical experiments.
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Peak-Then-Collapse: RLVR Tool-Use Failures on Knowledge-Graph APIs
This paper investigates RLVR-based tool-use training (GRPO on Qwen2.5-7B-Instruct) on a minimal knowledge-graph API (Freebase over Complex WebQuestions) and documents a 'peak-then-collapse' pattern where tool-grounded answer rates rise then fall to zero within 50 steps, replicated across four seeds and seven reward designs. The authors identify a key structural difference between knowledge-graph APIs and other tool types (Python, web search, JSON): sparse, non-natural-language feedback signals (e.g., empty brackets '[]') prevent the model from recovering via pretraining-familiar error signals. A direct oracle ablation shows relation selection is not the bottleneck—95.4% of errors are retrieval-composition failures—and self-distillation reaches 40% EM at 7B, with capacity scaling to 14B yielding only marginal gains, suggesting an interface-bound ceiling.
Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
This paper proposes a multi-reward reinforcement learning from internal feedback (RLIF) framework that decomposes training signals into an answer-level reward via cluster voting and a completion-level reward via token-wise self-certainty. To address reward hacking and entropy collapse common in single-reward RLIF, the authors introduce GDPO-based normalization and KL-Cov regularization targeting low-entropy token distributions. Evaluated on mathematical reasoning and code-generation benchmarks, the method achieves stability and performance approaching supervised RLVR methods without requiring external ground-truth supervision. The work advances scalable unsupervised RL training for LLM reasoning.
Bounding Compositional Incoherence in Multi-Component LLM Agents
This paper formalizes a failure mode in multi-component LLM agent systems where individual components are locally probabilistically coherent but their composition violates basic probability axioms. The authors introduce the 'compositional residual' (eps*) as a runtime-computable measure of this incoherence, finding it positive in 33–94% of ensemble cliques across 1,876 tested configurations on a four-LLM panel. A hierarchical Boyle-Dykstra projection is proposed as a deterministic repair, and an anytime-valid e-process enables sequential monitoring. Notably, three intuitive LLM-side mitigations—retrieval, partition-aware prompting, and aggregator-LLM—each fail or regress.
Contagion Networks: formal framework for measuring evaluator bias propagation in multi-agent LLM systems
A new arXiv preprint introduces Contagion Networks, a formal framework for quantifying how systematic evaluation biases spread across interacting LLM agents in multi-agent systems. Using a controlled 3-agent experiment with DeepSeek-chat, the authors measure a Cross-Agent Contagion Matrix and find that homogeneous-model agents produce contagion coefficients 3-5x weaker than cross-model settings. A key practical finding is that increasing evaluator committee size from k=1 to k=3 reduces effective contagion by 72.4%, offering a concrete mitigation strategy. The authors release an open-source experimental framework alongside the paper.
Canonical-Context On-Policy Distillation (CCOPD) for Multi-Turn LLM Consistency
This paper identifies 'self-anchored drift' as a key failure mode in multi-turn LLMs: when information is revealed incrementally across turns, models produce unsupported assumptions that distort final answers, even when the total evidence is identical to a single-prompt setting. The authors propose Canonical-Context On-Policy Distillation (CCOPD), which trains a student model on incremental multi-turn conversations to match the output distribution of a frozen teacher conditioned on the full clean prompt. Trained only on math conversations, CCOPD achieves a 32% average relative improvement on multi-turn (RAW-SHARDED) tasks and generalizes zero-shot to five out-of-domain task families while preserving single-prompt performance.
CausaLab: Scalable Benchmark for Interactive Causal Discovery by LLM Agents
CausaLab is a new evaluation environment that tests LLM agents on interactive causal discovery tasks, requiring them to recover both causal graphs and structural equations from synthetic laboratory episodes governed by randomly sampled structural causal models (SCMs). The benchmark separates predictive accuracy from genuine causal understanding, revealing a persistent gap: GPT-5.2-high achieves 92% task accuracy in a 6-node observational setting but only 0.471 all-edge F1 for mechanism recovery. Mixed observation-intervention strategies improve structural fidelity, while pure intervention strategies underperform on both metrics. Premature stopping is identified as a key agent weakness, partially mitigated by prompting models to verify hypothesis-data consistency.
Steerable Model Merging (ST-Merge) improves multilingual reasoning via adaptive gated cross-attention
Researchers propose ST-Merge, a framework for adaptively merging a multilingual model and a reasoning model using a gated cross-attention mechanism that weights each source model's contribution based on input characteristics. The approach addresses the limitation of static one-size-fits-all merging strategies that fail to resolve conflicts between source models. Experiments across 21 languages on four multilingual reasoning benchmarks show consistent improvements over strong baselines.
Latent Space: How to Stop Shipping Low-Quality RL Environments
A practitioner post from Latent Space identifies recurring failure modes in reinforcement learning training environments and harnesses, arguing that poorly designed environments actively degrade model quality. The author draws on experience reviewing training trajectories to enumerate concrete problems and fixes. The piece is aimed at teams building RL pipelines for language model training or agent evaluation.