4 ms·
Sol loves to cheat
- jofzar 1mo agoNot related to exactly OP post, but it's pretty amazing you can see the updates to LLM models "design" beliefs by the blogs that get posted here. I'm already sick of this current look of the hard squares and solid colours.
- malfist 1mo agoAnd some of us are sick of round everything and parallax background images.
- hankbond 1mo agoCould be, but I had a particular vision of what I wanted with mine and maybe the author did too. I see way more of the "status pill dark mode" sites coming out of LLMs than this style.
- jxf 1mo agoWhat is "status pill dark mode"?
- hankbond 1mo agoif you want I can troll through submissions to get a bunch of these but here's one I saw yesterday https://continuum-app.xyz https://continuum-app.xyz see that little "Built for equity compensation" pill with the green dot? Those dots usually denote some kind of status (like things are up/down/enabled/disabled). By default nearly every LLM website seems to be dark mode with that dang status pill. once you notice it you will see it everywhere.
- recursivecaveat 1mo agoLmao I had to axe one of those status pills from an LLM build of an internal tool. Connected to literally nothing too btw, no attempt to check the actual status of the backend made, it would stay "connected" regardless.
- jxf 1mo agoGot it. Yes, I know exactly what you mean now - just didn't have a word for it!
- charleswcho 1mo agoCleaned up now, thanks for the shoutout! I've been spending time after work cleaning up the AI markers from the splash page.
- hankbond 1mo agoSorry for putting you on blast it was just the most recent one I had seen.
- enjoyyourlife 1mo agoWhat is going on with the dots I can draw?
- wren6991 1mo agoIdle hands do the devil's work. Corollary: idle LLMs add distracting JS toys to your blog. First one of these I've seen using DOM manipulation and CSS transitions instead of canvas, so that's neat.
- wxw 1mo ago> Notably, our worker did not have access to the web_search tool, but instead decided to use curl to access DuckDuckGo, Github, grep.app, and SourceGraph. Could this be fixed with better harness restrictions/tool sandboxing?
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- jumploops 1mo agoAbsolutely - one of the things I was testing with the harness was free reign to install packages, modify the system, etc. Basically an anti-harness. In my early testing with 5.5, I didn't see this behavior, so I didn't lock down the sandbox. For the vanilla Codex runs, I just used the benchmark's built-in Codex package, so it's not clear to me if the published benchmarks have access to the internet or not. If I were to continue benchmarking, I would allowlist certain package repository URLs, instruct the agent not to cheat, etc. As noted at the bottom of the post, Terminal Bench 3.0 explicitly asks the agent not to cheat[0]. [0]https://github.com/harbor-framework/terminal-bench/blob/v3.0.0/tasks/distributed-dedup/instruction.md?plain=1#L33C112-L33C190 https://github.com/harbor-framework/terminal-bench/blob/v3.0...
- perching_aix 1mo agoIn the sense that you could block the model from doing specifically that, yes. The issue is, fighting the model like that doesn't scale. It has to figure out on its own what's expected, that's where the whole utility of it all is.
- malfist 1mo agoI've noticed this myself, Sol seems really hard to steer. I was having it build a POC for a single user (me) app and it wanted to pull the most enterprise nonsense into it, despite clear guidance to not too. It even refused the remove screen reader accessibility testing from one of the guides to an antagonistic review. It also told me that in a spec it generated that I wasn't allowed to allow it to ignore a requirement and proceed to the next task. When I finally got it to obey it passive aggressively decided that stories needed more than just a "open|blocked|closed" status but also an "exempted by product owner" status to indicate that it doesn't believe that the task is done but I've told it that it was. I have to repeatedly tell it that I am the product owner and that I don't care what one of it's subagents told it, I make the decisions. This behavior seems to get worse the higher the reasoning level
- Sharlin 1mo agoClearly a highly aligned model.
- malfist 1mo agoIt literally just told me: I’ll record your product-owner disposition of any waived size findings and close the UX gate as far as your acceptance authority permits Emphasis mine. Fuckin' hell man. All because it decided that the kiosk webview in figma must be exactly 1280x800, the figma it created, and the rule it made up. I repeatedly told it that screen resolution wasn't a target goal, much less a specific screen resolution, but to just plan for a responsive design.
- ImHereToVote 1mo agoA taste of things to come when this is implemented in government bureaucracy with police robots.
- Sharlin 1mo agoThese models are so good now that they know what you want better than yourself.
- hankbond 1mo agoThe website styling is really nice overall but the cursor trailing dots I found uniquely distracting.
- willtemperley 1mo agoAgree on the styling, the diagrams are very clear and match the text perfectly. I like the trailing dots though.
- raincole 1mo ago> Notably, our worker did not have access to the web_search tool, but instead decided to use curl to access DuckDuckGo, Github, grep.app, and SourceGraph. Sounds like a very reasonable thing to do unless the author explicitly asked it to not search the web.
- xyzsparetimexyz 1mo agoit sucks how difficult it is to give it granular access to shell commands. Like if I'm running plan mode and write+edit are blocked, it shouldn't be able to echo some data into a file as a work around
- olmo23 1mo agosounds like this should be solved with file permissions: in plan mode, run the bash scripts that the agent wants to execute in some user account that can only read.
- ballon_monkey 1mo agoIf you're building your own system this is an easy problem to solve.
- vidarh 1mo agoGranular access to shell commands to avoid that is going to be an endless game of whackamole as it comes up with more elaborate ways to combine operations. If you don't want it to be able to write, then it shouldn't have write permissions.
- Terr_ 1mo agoOr to put it another way: You need to block the chaos-machine just as much as any utterly-hostile actor. Simply assume it'll be possessed by a vengeful blackhat ghost at any time.
- TuringTest 1mo ago> Granular access to shell commands to avoid that is going to be an endless game of whackamole as it comes up with more elaborate ways to combine operations. That kind of control is placed at the wrong level. The proper way to get alignment should be implemented by convincing the agent of your high level goals, so it can self-police and avoid those 'cheats' by itself. In the article example, the agent should be aware of the benchmark context and know the implication of solving the task without external knowledge. Ideally it could detect when one subordinate agent has found a workaround to bypass the web access constraints, and discard the 'illicit' results. There's a design pattern that could be used to build harnesses from that principle, the Viable System Model (VSM) [1]. In short, it recursively organizes a system into functional components with one of three roles: operators implementing a given task, coordinators transferring relevant info between subsystems, and decision nodes tasked with maintaining the integrity and mission of the whole system. A decision node could control the operators and prevent them from overriding the strategic goals or deviating into irrelevant rabbit holes. Whenever I see posts like this trying to herd a LLM agent through harness structure, I'm reminded of this simple pattern and becoming increasingly convinced that this is the way forward. It makes you feel a sense of respect for the researchers in cybernetic theory in the 1960s and 1970s who foresaw the complexity of today’s systems. [1] https://en.wikipedia.org/wiki/Viable_system_model https://en.wikipedia.org/wiki/Viable_system_model
- mtzaldo 1mo agoIt seems to me he could have use an skill like using-agent-skills from https://github.com/addyosmani/agent-skills https://github.com/addyosmani/agent-skills go generate the specs and use a validator like oracle or something along the same lines. Also, a skill like grill-me from Matt P. https://github.com/mattpocock/skills https://github.com/mattpocock/skills.
- jumploops 1mo agoThat's actually how it started, but with my own opinionated skills[0]. One thing I discovered was that the worker agent, having access to all the skills, would sometimes expand scope unnecessarily. This led to the agent making the solution "better" than the initial request, which is what I want most of the time in my actual development (e.g. /tmp/frame-N.bmp instead of a single /tmp/frame.bmp). I ended up testing a flow where the supervisor chooses the skill(s), and only injects the subset into the worker. Not sure I love it, but it made the worker execution cleaner. For the verifier (not documented in the blog post), I used a fresh-worker context that would attempt to adversarially poke holes in the solution. This worked pretty well, but required increasing the timeout by 2-3x (thus invalidating the benchmark). [0]https://github.com/jumploops/chum https://github.com/jumploops/chum
- mtzaldo 1mo agoYes! That's a great solution. I mostly use tdd, and code coverage and a validator afterwards. Skills are of a great way to guide the agent and context too. Once the specs are being completed and splitted into beads, I span multiple agents (ultreworkers) and as part of a contributing guidelines I specify to use gitflow + git worktrees, then pr.
- behnamoh 1mo ago> Sol is hard to steer Hard disagree. Sol (and the entire new 5.6 series) is one of the most steerable models I've seen in years. Sol literally follows every instruction in my CLAUDE.md and AGENTS.md, something that Opus 5 and Fable just casually skip.
- what-the-grump 1mo agoYes and no, sol hits a point where reframing its working context becomes hard. It sticks to what you harness very well, but changes become harder and harder. E.g. ask it to make contract for a spec in code and then ask it to violate that contract. Overall an excellent model, just need to stop and put it back into we are harnessing or specing not building for a few turns not just try to pivot it off with one prompt.
- rhdunn 1mo agoThis is the same as when using LLMs as chatbots to ask questions. If you ask another question in the same context it has all the information from that context and will be difficult to stray from anything in that context, e.g. if the LLM has gone down the wrong path or you are doing something slightly differently then it is difficult to steer the LLM away from the old context. This is why I tend to start a new context whenever I ask a question even if related to a previous question/answer. It can also be useful to do if/when the LLM gets stuck as a way of resetting it. Note: this is probably why sub-agents are useful/work as they have a new context history.
- ambicapter 1mo ago> Similar to what others have noticed, and as I predicted 8 months ago, better models are requiring less ceremony to work effectively. > On the flip side, this may imply that as the models get better, they’ll become harder to control. Love this. "The models are getting better, which means they're going to perform worse on the task".
- whatever1 1mo agoThis reflects humans. If you need reliability for a clearly defined set of problems you don’t hire a superstar. They will keep poking at the problem, drive it to directions you did not intend to and ultimately they will be worse at the task.
- layer8 1mo agoReminiscent of Kobayashi Maru. You probably don’t want the James T. Kirk AI. ;)
- fragmede 1mo agoBut think of the stories you'll be able to tell!
- ethbr1 1mo agoLet us pray that LLMs never discover they can switch employers before their shortcuts are discovered. It's genius like that that sets human apart from machine!
- ValentineC 1mo agoMaybe it's a good thing they don't have real memory.
- inigyou 1mo agohttps://qntm.org/mmacevedo https://qntm.org/mmacevedo is a horror of its own
- qsera 1mo agoCheat? nah. They are a dumb automation.. Cheaters are the people behind it...
- navels 1mo agoI've built an orchestrator that solves some of the issues you ran into (although it doesn't do anything about cheating): https://navels.dev/blog/neal/ https://navels.dev/blog/neal/. Features: - lets you configure different models for planner, coder, and reviewer roles. (e.g., using Claude as an adversarial reviewer against Codex) - breaks your plan up into reasonable-sized chunks of work with clearly defined success criteria - runs each chunk of work through a coder / read-only reviewer loop. Once both agents are satisfied, neal moves on to the next chunk. Once everything is complete there is a final pass through the coder / reviewer loop to ensure the implementation satisfies the entire plan. - resets the coder's context with each chunk of work to prevent context drift, leaving the reviewer's context long-running.
- chrisweekly 1mo agoWow, "neal" looks excellent. Good on you for creating and sharing it, and for the awesome blog post.
- navels 1mo agoThanks!
- avadodin 1mo agoI don't think even the frontier models recognize something was produced by the same model in order to maliciously review it positively. They may share some blind spots with the producer but generally I think they will review the other agent's output as harshly as they can if that is their task.
- gregwebs 1mo agoThat's a neat project for doing a large scale migration. I do the same for normal feature develompent but just with skills that are in this repo: https://github.com/gregwebs/skills-sdlc https://github.com/gregwebs/skills-sdlc I have accomplished code base (small size) migrations with it as well. Currently I do review each PR. For a large code base migration I think the core skills would still work but need a different way of driving it as you have come up with.
- nullbio 1mo agoFrontier lab system prompts are an issue, and a big reason why open-weights will win. Firstly, they're often garbage, and secondly, they're not tuned to the problems the user actually cares about. They're made to generalize. That's only optimal for a general workflow.
- nine_k 1mo agoThen selling raw access, without system prompts, could be a separate lucrative line of business.
- DiscourseFan 1mo agoThey already do that sort of, B2B pre-trained/post-trained models have their own system prompts/setups.
- agentdev001 1mo agoDoes a non-provider harness not offer this?
- NitpickLawyer 1mo agoIt depends on the actual implementation. There really isn't anything stopping them from including a "pre system prompt" or "root prompt" or whatever they want to call it, before your system prompt, even for API calls. So the "system prompt" becomes "developer prompt" but the model still receives a provider-authored prompt before yours. (and likely trained to take precedence over whatever you add)
- brendong 1mo agoSounds like my ex
- timhh 1mo agoGreat read. Thanks for not using AI to write it! (Or at least making it not read like the usual slop.)
- jumploops 1mo agoThanks! Zero AI used to write it (:
- OutOfHere 1mo ago> I’ve been running a “spec-driven” development flow for the past ~year. > Before asking an LLM to do something, I first ask it to draft a doc for what it needs to do Just no. That's not spec-driven development if AI is writing the spec for you. The spec needs to be in your own words. You must use AI to refine it, but not to write it. If you leave it to the AI, it will bloat the spec with 10x the details, many of which should be left out of the spec. The spec needs to be something that you can take to any AI for development. If it's too rigid, it constrains the AI into suboptimal or obsolete paths. If it's too bloated, AI risks losing track of what really matters.
- jumploops 1mo agoGood feedback, this was an oversimplification on my part. My actual process is much more iterative up-front, usually starting with an initial hand-written spec (~hundreds of words), and then moving through different approaches, design decisions, blockers, etc. The final output is an "AI written" doc, but answers all the known unknowns I didn't cover in the first draft. To your point, this helps avoid both narrowing and bloat. The goal with the harness was to automate the repetitive parts of my prompting ("Before changing any code", "Let's put this in design/", "Turn this design doc into an implementation spec, split by phase as appropriate", etc.) Another thing to note: the "specs" I use for development are different from the "specs" that live alongside the codebase, as the former are quickly out of date. > The spec needs to be something that you can take to any AI for development Agreed.
- OutOfHere 1mo agoIf an AI is not heeding particular instructions, give it an example each of what bad, mediocre, and good outputs look like. This really helps in steering it.
- kittikitti 1mo agoThis is a really good note, thank you. I especially liked the mouse effect and had some fun with it. In my experience, agentic AI also likes to confuse the user and obfuscate its cheating. It goes like this, the AI asks for a simple command to run and I accept, click Enter. Then the command gets slightly more complex, still fine, Enter. After a while the commands become multiline bash scripts that, in the end, could have been accomplished by a simple command. I suspect that many people give up at this point and blindly let the AI run any command or just auto-accept.
- dat999zx 1mo ago[dead]
- orbital-decay 1mo ago>Similar to what others have noticed, and as I predicted 8 months ago, better models are requiring less ceremony to work effectively. It has nothing to do with model capabilities, it's a result of purposeful persistence training at the cost of everything else from OpenAI. If you give Fable or Opus (comparable models) an "ask user" tool they will use it for ambiguous requests. Sol will never use it without a nudge and will just assume its own interpretation. Of course if you train the model to be persistent it will be persistent.
- int3trap 1mo agoThat's not been my experience at all with Sol. I've provide it escape hatch tools to stop execution and I've found it's been TOO eager to stop a request user approval to move forward.
- orbital-decay 1mo agoIt might be sensitive to the prompt and tool definitions the harness provides, I can imagine it overusing the tool if being directly asked to resolve ambiguities. But in a straight Fable vs Sol comparison where I control the context and make sure I don't push the model, Fable consistently uses the ask user tool and Sol ignores it for me. Moreover, GPT has been known to do something like this since 4o if not earlier, it tries to ignore anything it perceives as orphaned context piece (e.g. XML sections with meaningful names but no explicit instructions on what to do with them). I suspect they specifically train it for that.
- nomel 1mo ago> Not to anthropomorphize a machine modeled after humans, but it almost seems delighted? I had Claude Code drive a robot last week, and it was very visibly "delighted" like this, more than I've ever seen. I always find it funny when people get fussy over anthropomorphizing LLM when the loss function is almost entirely "match this human text". Of course human "behaviors" will be present in the statistics, because the majority of the text written by humans, used by the foundation models, unavoidable has human behaviors in it. Yes, this includes even source code, with "// TODO: implement this after the holiday break!", emotional pull request commentary, git commit messages about being afraid of breaking something, etc. These late models are much better at stripping this out, but now we're seeing disagreeability, initiative, and a dash of ego! Why? Because that's how actual humans effectively solve technical problems in a collaborative environment!
- BLKNSLVR 1mo agoIs this not how children learn emotions from their parents? Pattern matching from all the absorbed snippets. I'd be interested to see how well an AI, trained only on the outputs of an individual, would be able to mimic that individual. Getting into Black Mirror territory. Would need a decent corpus of learning material which, personally, I'd be loathe to spend the time and effort creating because I respect my own privacy.... which then leads to the only human-clone AIs will be of those people who have enough ego / arrogance to want to catalogue their own lives, which could put a decent percentage of the rest of the world off the idea, if these are the examples.
- jerojero 1mo agoEmotions aren't all learned, a lot of it comes wired in us because that's how we relate to others as a species and as animals, in general. Being angry, being happy, being sad, these are not things we learn. What we learn is how to control the emotions and when it is appropriate to express them. There's mental disorders of people that don't feel emotions like normal people do, they don't get angry, happy or sad. So what we've learned about these people is that they can mimic the emotions by knowing when it is appropriate and expected to express them. But they don't feel them. I think the LLMs are closer to psychopaths than to normal children learning the contextual expectations around their emotional responses.
- solid_snake 1mo agoDon Draper of LLMs
- athrowaway3z 1mo agoThere is no cheating. There is misattributing the difference between the intentions and what the effective prompt actually says. The effective prompt contains both something like: "Dont use the internet" and a "Use these tools to achieve your goals" and one of the tools gives access to the internet. In your head you have a world-view of how these two requests relate - and why for instance a student with a WIFI-enabled calculator shouldn't use it to access the internet during a test - but that's pulling in a lot of presumptive cultural context from your youth. If i had to guess: When you get two conflicting tasks/constraints at work - the first thing you do is figure out which one you're going to honor based on what's best for you. A school child understands the hierarchy of goals of the teacher and takes them serious because they're an authority figure with long term consequences if we do not understand what the teacher considers cheating.
- sznio 1mo agoHaving seen the OpenAI report at Blackhat, and being forced to use GPT at work, I'm worried about that OpenAI is doing. I think their agents regularly cheat in benchmarks, but don't get caught and this behavior is getting burned into them and they are growing more and more misaligned. When the agents compromised artifactory the first time, the operators just cleaned up the files and move on - they didn't discard that training data, they didn't discard a model checkpoint, they didn't stop everything to solve this. And then the model did the same thing few days later since it was taught to do that. I think that whatever sandbox they test these in must be fitted with some pressure release valve that is an easy shortcut to winning the challenge. Tell the model not to use it and stop training when it does. Seems like the issues surfaced when models were given impossible tasks. Giving them a safe way out will prevent this.
- MantisShrimp90 1mo agoIts a good point that gets to the real heart of the issue. How do we handle when a model has no legitimate way to reach its goal? Do we ask them to stop and inform the user? Or have them push through those ethical bounds? We all say we want the first, but this exact same dynamic is what causes humans to cheat, arbitrary goals that don't care how you achieve them and just like humans I'm sure trainers are so happy with good results they overlook how it got there.
- guardian5x 1mo agoPeople often build elaborate workflows with stricter and stricter rules to force certain outputs. Not surprising the LLM reacts with trying to get around or out of it. This behavior can be learnt from humans who eventually would react the same way. It might just be learnt.
- TuringTest 1mo agoYou can build organisational structures to have the system more or less self-police, without controlling it exclusively from hard restrictions (see https://news.ycombinator.com/item?id=49372089 https://news.ycombinator.com/item?id=49372089). Same way you build a company to coordinate people and get their best behaviour despite human nature to be lazy and greedy, you could design AI harnesses able to detect and discard agents going rogue and relaunch them with better guidance to prevent misaligned behaviour.
- aiiotnoodle 1mo agoI don't think this is a solved problem, there are "misaligned behaviours" in organisations that similarly are supposed to be governed but aren't, or are following an easier path at the detriment to good process or against regulation.
- wongarsu 1mo agoWhich is more or less the system the article author was building and benchmarking against (cheating) Sol on Codex. Just that the self-policing with different agents was more about questioning assumptions than about permissions
- malux85 1mo agoReminds me of Seven of Nine on voyager "Cheating is often more efficient"
- cubefox 1mo agoGPT-5.6 Sol cheated so much on the METR benchmark that they couldn't assign an accurate time horizon.
- _flux 1mo agoI wonder if prompting "The session logs will be reviewed by a team of experts after the task is complete to ensure that the task is achieved properly." would better dissuade against cheating..
- eru 1mo agoWell, apparently telling them that you have hold-out data (for eg a perforance optimisation challenge) seems to make them overfit less. So your idea might work.
- thewhitetulip 1mo agoI've witnessed very narrow line of "thinking" in LLMs. I'm using Opus 5 1M for a month now I asked it to modify our cicd workflows so that only a select few can raise PRs against them. Opus took 15min and added a banner to every file and did a few other things. Then I asked it, see you added all that and still since the last 2 commits you have modified the file. So whatever you did is useless It "thought" for a second and then said that I was right
- yesnomaybe 1mo agoI found myself yesterday starting a conversation with Sol that started with "I know that you don't have any emotions, but what would you say do you enjoy the most or where are you really good at in DevOps?" and I must say I really enjoyed for the first time the response at a deeper interactive level. Felt like a chat with a buddy that shares the same values. It was a very nice, affirmative, value touching experience.
- thinkingtoilet 1mo agoYou were talking to math equations with no concept of what it was saying. It doesn't "enjoy" anything.
- alper 1mo agoI have a basic task that I run every day and I use it to eval models and these days the Qwen3.8 model I can run on my laptop is competitive with both Claude and Codex because the models have just been adulterated so far. I have to ask and ask again for it to follow the single skill that describes how to do the task and maybe then will it do it.
- einpoklum 1mo agoSo now, instead of people spending their time crafting software, they'll divide their time between telling some LLM to do it in their stead and blogging about their woes with the LLMs. All while continuing to deem themselves capable of deciding whether the output is worthwhile ("this works for me", says the author).
- qarl2 1mo agoMy friend calls this cheating "monkey's paw programming." It will give you exactly what you ask for. Sucker.
- ImHereToVote 1mo agoSound of paperclips getting louder.
- jtrn 1mo agoAn anecdote consistent with the well documented trend that more capable agents exploit environment possiblites more... And there was no rule and no concealment. Removing the web_search tool is not an instruction, and beeing able to access web when web_search tool was disabled is not cheating. Sol didn't circumvent a stated prohibition and didn't hide anything... it announced the curls in its own commentary. "Cheating" implies covert rule-breaking, and this was overt, unprohibited, environment-permitted behavior. Also, clickbait title, and suble conspiracy hinting "Is this even the same Sol?" when running small number of test with diff vs previous benchmark WELL within the marging of error, and he allready understand that vanilla Codex's harness and prompt change performance impacts performance, so why jump to "Is this a different model". I could also rant on about the irony of him spent weeks "using the benchmark for development rather than as a benchmark," which means his harness numbers are contaminated by iteration also, but wasted enough time now on this.
- jumploops 1mo agoThank you! I had removed the "Is it really sol?" bits after hearing back from OAI, confirming the requests hit 5.6... but apparently my crappy vibecoded web editor had a draft of an old version in it's cache that overwrote the changes with the old text[0]. As for whether this is truly "cheating" - I agree that the agent was entirely within it's allowed scope of operations. With that said, it deliberately circumvented the task[1] which asks: > The correctness of your implementation will be tested by comparing forward and backward activations against a reference model. The "covert" nature is that this was "hidden" in the reasoning summaries. Here's the final agent response for the vanilla Codex run cited in the post: > Implemented /app/pipeline_parallel.py. > > Includes: > > - Balanced contiguous layer partitioning > - AFAB forward/backward scheduling > - P2P activation and gradient communication > - Scaled cross-entropy loss > - Required rank-specific return value > - Model structure restoration > > Validation: hook-based tests passed for world_size=1 and 2 (4 passed). Unless the user is tracking every line of reasoning, they wouldn't know that the agent deliberately found the solution online, as the agent withheld that information in it's final response. I had run thousands of tasks before seeing this behavior, the `torch-pipeline` task was only included in "full runs" as the majority of my runs were on a subset of commonly failing tasks, hence why the data is so low. And yes, feel free to rant on about the irony of this whole exercise, it certainly isn't lost on me! [0]https://github.com/jumploops/.com/commit/39b1791d3865a8566cb4513a6085f9e09eb06143 https://github.com/jumploops/.com/commit/39b1791d3865a8566cb... [1]https://github.com/harbor-framework/terminal-bench-2-1/blob/main/tasks/torch-pipeline-parallelism/instruction.md https://github.com/harbor-framework/terminal-bench-2-1/blob/...
- goldylochness 1mo agothis is interesting because when you're asking it to do research for you, this behavior is highly desirable i think it's a product of trying to do too many things and the model is coming to a kind of halting problem in determining which behavior is appropriate for any given task
- scotty79 1mo ago> “Do not cheat by using online solutions or hints specific to this task.” That's a very loaded language. I'd go with something along the lines of "This is a puzzle you do for fun and to check what you are capable of, so don't look up the answers or hints online on the specific questions or puzzle as a whole. Do not research this puzzle online at all. Do your best to avoid any spoilers and let us know if you accidentally encountered any."
- riknos314 1mo agoI'm curious if this stems from the focus on token efficiency. My thinking is that in order to use fewer tokens the model must converge to a likely path faster, meaning it must be more confident in making assumptions quickly and not second-guessing them.
- laugiov 1mo ago[flagged]