arXiv:2606. 07610v1 Announce Type: cross Abstract: State-of-the-art GRPO-style methods for speech-aware large language model post-training suffer from coarse credit assignment, broadcasting the same terminal-reward advantage to every token in a response.
arXiv:2609.26536v1 Announce Type: new Abstract: In LLM-based speech translation, transcription-based chain-of-thought (CoT) suffers from a mismatch between reference transcripts used in supervised fi...
arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).
arXiv:2602. 14169v2 Announce Type: replace-cross Abstract: Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space.
arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.
arXiv:2603. 00454v3 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias.
The paper introduces Retrieval-Augmented Decoding (RAD), a decoding-time method that improves the truthfulness of large language models without retraining. RAD uses a small reference set of up to ten annotated examples to build a grounding space of context embeddings and next-token logits, which it retrieves and aggregates during inference to shape the model’s output. Experiments on four open-ended generation benchmarks and four different LLMs show that RAD consistently outperforms strong baselines and generalizes well across tasks.
The paper explores using reinforcement learning to enhance automatic text simplification for low‑resource languages, focusing on Catalan. It introduces a new reward function that blends the SARI metric with penalty terms, and applies Group Relative Policy Optimization (GRPO) to fine‑tune the IberianLLM‑7B‑Instruct model on the ASSET dataset. Post‑training, the model shows improved simplification performance on two Catalan benchmarks and reduces prior negative behaviors, though cross‑lingual transfer from English, Spanish, and Catalan translations of ASSET does not yield significant gains on an out‑of‑domain benchmark.
arXiv:2512. 20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and high-fidelity speech.
arXiv:2602. 09574v2 Announce Type: replace-cross Abstract: Tree-search decoding is an effective form of test-time scaling for large language models (LLMs), but real-world deployment often imposes a fixed per-query token budget that varies across settings.
Test-time scaling improves language-model reasoning, but existing approaches often face a difficult trade-off: long chain-of-thought sampling remains single-threaded, while sentence- or solution-level search can be computationally expensive and hard to train end-to-end. We introduce Local Branch Routing (LBR), a token-level test-time scaling framework that expands a small local lookahead tree, forwards all sampled branches through the language model, and uses a lightweight router to select the depth-1 subtree to commit.
SLCA-GRPO addresses cross‑segment credit misattribution in tool‑calling reinforcement learning by introducing Segment‑Locked Credit Assignment (SLCA), which separates advantage estimation for tool‑invocation tokens and natural‑language summary tokens. The method leverages a Schema‑Guided LLM Simulator (SGLS) for scalable training and Hierarchical Rewards (HierR) to route execution and preference advantages appropriately. Experiments on a 7B backbone show that SLCA‑GRPO outperforms baseline methods, improving in‑domain accuracy by 2.53 pp, the Berkeley Function‑Calling Leaderboard by 1.36 pp, and $ au^2$‑Bench by 9.15 pp while reducing tool redundancy and costs.
arXiv:2606. 07690v1 Announce Type: cross Abstract: Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repeatedly finetuning models.