SFT

2026-10-23

RESEARCH · 1 SOURCE · arXiv cs.AI

Evidence-driven multi-agent AI (DRL + LLM) for head & neck proton PBS radiotherapy planning (arXiv:2610.29010v1)

This arXiv preprint proposes a compound AI system that combines a PPO-trained policy network to generate multiple proton PBS treatment plans with an LLM-based multi-agent conversational refinement layer. The planning agent (based on Qwen3-14B and post-trained with SFT and GRPO), a chatbot interface, and an evaluation agent were tested on 21 head-and-neck cancer patients (5 PPO plans each; 50 plans selected for instruction-guided refinement), and the post-trained Qwen3-14B improved instruction-following, retrieval and domain reasoning to produce clinically more appropriate trade-offs during user-guided plan adjustments.

6.0

2026-10-22

COMPANIES · 1 SOURCE · AWS Machine Learning

AWS launches SageMaker AI serverless 'Training Sessions' (GA) for model customization

Amazon announced general availability of Training Sessions for SageMaker AI serverless model customization, letting users run custom post-training loops (SFT, DPO, RLAIF, multi-turn RL, LoRA or full fine-tuning) without managing GPU infrastructure; SageMaker handles GPU allocation, distributed execution, checkpointing and fault recovery and offers a cookbook and agent skills for end-to-end workflows. Customers cited use cases including healthcare claim-denial triage and multi-turn agent training with tools like Amazon Neptune and gpt-oss-20b.

7.0

2026-10-21

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv paper introduces SPOKE to improve strategic play of compact poker agents

The paper (arXiv:2610.27028v1) proposes Strategic Preference Optimization with KL-anchored Equilibrium (SPOKE), which combines teacher-relative value penalties, policy anchoring, risk constraints, and structured rationale supervision to improve strategic play under hidden information. Evaluated against SFT, DPO, GRPO, and GSPO on frozen models (SmallThinker, Qwen3, Gemma-3) and three runs of a SmallThinker-4B-A0.6B policy, SPOKE yielded lower mean exploitability in Kuhn (0.213) and Leduc (1.081) and higher six-max top-1 agreement (0.775) while maintaining teacher KL similar to SFT.

5.0

2026-10-20

MODELS · 1 SOURCE · AWS Machine Learning

GPT-OSS-20B fine-tuned for multilingual reasoning on Amazon SageMaker HyperPod

Teams fine-tuned the open-weight reasoning model GPT-OSS-20B on a multilingual chain-of-thought dataset (HuggingFaceH4/Multilingual-Thinking) using supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) on Amazon SageMaker HyperPod with EKS, LoRA, TRL/veRL, vLLM, Ray/KubeRay and Amazon FSx for Lustre. Results: the base model almost always produced English chains-of-thought; SFT corrected most of the language behavior gap, and GRPO provided additional improvements only when the desired behavior had non-zero probability in the base model; a Qwen2.5-7B control experiment supported the same conclusion.

6.0

2026-10-07

RESEARCH · 1 SOURCE · arXiv cs.AI

SSRFT: Safe-Role Internalization for Robust LLM Safety Alignment (arXiv:2610.07023v1)

The paper introduces SSRFT (Supervised Safe-Role Fine-Tuning), a safety-alignment framework that teaches models to internalize a predefined safe role by building a Safe-Role Q&A (SRQA) dataset from psychometric questions, limited jailbreak prompts, and a safe-role description; role-consistent responses are synthesized, validated, and expanded into diverse scenarios. Experiments on multiple Base and Instruct models report that SSRFT yields more robust and generalizable safety alignment than standard SFT, with greater resistance to prefilling attacks, better generalization to unseen jailbreaks, reduced over-refusal on benign queries, and preserved general capabilities.

6.0

2026-09-18

RESEARCH · 1 SOURCE · arXiv cs.AI

Paper proposes 'checkpoint handoff' to separate REACH and SOLVE effects in agentic RL

The arXiv paper (2609.19636v1) introduces 'checkpoint handoff', an evaluation protocol that clones states reached by one checkpoint and runs another checkpoint from those identical states to decompose endpoint success into REACH (arriving at useful states) and SOLVE (finishing from identical states). Applied across two benchmarks and two released pipelines (including experiments on ALFWorld), the method shows positive interaction: RL histories help RL solvers more than SFT solvers, and independent REACH and SOLVE gaps predict aggregate gains.

7.0